Collaborate with us
Are you searching for students to your research group, project, company or organization? Please contact Jan-Erik Moström, Director of Studies at the Department of Computing Science.
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Below you find a list of degree projects in collaborations with our research groups or external partners.
Are you searching for students to your research group, project, company or organization? Please contact Jan-Erik Moström, Director of Studies at the Department of Computing Science.
Open Set Learning and Anomaly Detection in Vision Tasks (15/30hp)
We are announcing projects focused on open set learning, anomaly detection, and out-of-distribution detection. These areas are crucial for developing robust and reliable algorithms, particularly in applications where the ability to handle unexpected or novel inputs is essential. By leveraging deep learning (DL) and machine learning (ML) techniques, this project aims to advance the state-of-the-art in vision tasks, with potential applications in medical imaging and robotics.
For instance, consider a robotics scenario where an autonomous robot navigates through a dynamic environment. Traditional models might struggle with objects that were not present in the training data, such as new furniture or unexpected debris. This project will explore methods to identify and handle such cases, ensuring safety and efficiency when faced with unforeseen obstacles.
We are looking for motivated students who have previously taken courses in DL and computer vision, and as a result, have experience with TensorFlow and/or PyTorch. Interest in embodied AI (when AI has a "body" and as a result interacts with the environment, the way robots do) is a bonus. Ideal candidates should have a strong background in these areas and a keen interest in applying ML to real-world problems.
More info: Polina Kurtser polina.kurtser@umu.se
Path Planning and Object Manipulation with Industrial Robotic Arms (30hp)
We are announcing a new MSc project focused on path planning and object manipulation using industrial robotic arms. This project will leverage reinforcement learning (RL) to learn sequences of tasks, and potentially incorporate large language models (LLMs) and foundation models to break down large tasks into smaller, manageable tasks.
For example, consider a scenario where the goal is to stack cubes. The command to “stack cubes” can be broken down into smaller tasks such as identifying the cubes, planning the path to pick up each cube, and placing them in the correct order. Each of these smaller tasks can be assigned a score based on an RL cost function, which evaluates the efficiency and effectiveness of the task execution.
We are looking for motivated students with the following skills and interests - experience with ROS (Robot Operating System) for both practical and simulation environments, comfortable with Python/C++, previous coursework in robotics and a strong interest in the field are considered a bonus.
Ideal candidates should have a solid background in these areas and a keen interest in applying advanced AI techniques to real-world industrial applications. If you are passionate about robotics and eager to contribute to innovative solutions in industrial automation, we encourage you to apply.
More info: Polina Kurtser polina.kurtser@umu.se
Multimodal Machine Learning, Reasoning, and Compositional Generalization (15/30hp)
Är du intresserad av att utveckla AI-system som kan resonera effektivt över flera modaliteter, såsom bilder och text? Medan nuvarande multimodala modeller som CLIP, LLaVa och GPT-4v visar imponerande prestanda på en rad olika uppgifter, lider de också av tillförlitlighetsproblem. Kompositionell generalisering, eller förmågan att kombinera kända koncept på nya sätt, är en sådan utmaning. Till exempel bör en modell förstå att färgen på ett objekt ofta inte avgör dess storlek. Många befintliga modeller har svårt med detta slags resonemang och förlitar sig istället på statistiska mönster i träningsdatan snarare än en verklig kompositionell förståelse av världen.
Detta projekt syftar till att utveckla multimodala modeller (t.ex. genom att kombinera språk och visuell information) som kan resonera mer effektivt och generalisera bättre över olika uppgifter. Du kommer att undersöka metoder för att förbättra kompositionell generalisering och/eller skapa bättre ramverk för att utvärdera dessa förmågor. Exempel på uppgifter inkluderar frågor om bildinnehåll och visuell matematisk resonemang, där möjliga metoder att utforska inkluderar neuro-symboliska metoder som kombinerar neurala nätverk med exempelvis ramverk för logiska resonemang.
Oavsett om du är intresserad av att implementera nya metoder, utvärdera befintliga modeller på nya sätt, eller närma dig detta från ett tvärvetenskapligt perspektiv (som kognitiv vetenskap), erbjuder detta projekt ett brett område att utforska. Om något av dessa ämnen intresserar dig, kontakta Adam Dahlgren Lindström på dali@cs.umu.se för mer information.
Visualization and processing of atmosphere data (15/30 credits)
Researchers within space physics and geophysics are facing new challenges when three-dimensional data from radar measurements and photogrammetry of atmosphere and ionosphere become available with emerging technologies. Within the research project VisA, methods and tools for visualizing volumetric authentic data are developed to make the data accessible for researchers and the general public. The huge amount of data that is processed (both in time and space) entail requirements on new and effective algorithms as well as new ways to present the results. To visualize space phenomena, like Northern lights, we are using the open source planetarium software OpenSpace and for targeting the researchers we develop custom made tools.
Examples of Bachelor’s and Master’s Thesis projects are:
Within OpenSpace we have a close collaboration with Visualiseringscenter C in Norrköping. Therefore, a Master’s thesis can be conducted as a joint project with them that may include longer or shorter stays in Norrköping.
For more information
If you think this sounds interesting, please contact Stefan Johansson, stefanj@cs.umu.se.
Machine learning model for medical imaging in ovarian cancer
Machine learning has become very important in medical sciences as a means to automate for instance diagnosis and prognosis, and to personalise treatment. Machine learning can be used in medical imaging analysis and has the potential to function as a clinical decision support tool in cancer diagnostics. In collaboration with the Department of Radiation sciences, Diagnostic radiology, and Umea University Hospital, Radiology and Nuclear medicine department, we plan to set up a machine learning model for standardised classification of suspected ovarian cancer in magnetic resonance tomography (MR). We are therefore looking for Master's thesis students who are interested in taking part in the development of such a model based on locally annotated images. The method will subsequently be validated on a prospective image material from a study where the inclusion has started and will be ongoing between 2023-2027.
For more information
If this sounds like something for you, please contact Tommy Löfstedt via e-mail: tommy@cs.umu.se for more information, on this and other potential projects.
HPC TTN contraction for machine learning real-time applications (30hp)
In a machine learning context, the prediction of a Tree Tensor Network (TTN) learner is performed via a contraction of the full network with a (tensorised) sample of the dataset. This contraction can be carried out in many different ways and allows parallelism and different exploits such as:
Having an optimal strategy for fast predictions is of the uttermost importance in every ML scenarios but especially in real-time deployment of ML models. Some of the most prominent applications field range from: object-detection in autonomous-driving, on-line data processing in manufacturing, big data finance analysis, etc.
More info:
https://hpac.cs.umu.se/~pauldj/tmp/MS_thesis-TTNContraction.pdf .
Contact Paolo Bientinesi for more info pauldj@cs.umu.se
HPC TTN Riemann optimisation
In a machine learning context, an interesting paradigm for Tree Tensor Network (TTN) models training is the Riemann optimisation (see https://doi.org/10.1088/1367-2630/ac0b02). With this technique, derived from differential geometry, one can optimise the complete network at once allowing direct implementation of different gradients and stochastic gradients methods. Moreover the numbers of trainable parameters can be dynamically adjusted and thus giving a better training convergence and resilience to overfitting. Technically the Riemann optimisation involves a multiple stacking operations between tensors (thus data manipulation) that are not well supported by many tensor libraries.
For more info
https://hpac.cs.umu.se/~pauldj/tmp/MS_thesis-TTNRiemann.pdf
Contact Paolo Bientinesi for more info pauldj@cs.umu.se
Ericsson Internship/Master Thesis: Automatic segmentation for semantic SLAM dataset
Motivation
The Efficient understanding of both geometric and semantic characteristics of the environment is needed so that robots or XR devices can appropriately perform requested tasks and understand the local physical environment. Simultaneous localization and mapping (SLAM) algorithms allow the XR device to simultaneously geometrically map the environment and localize itself within the environment, while object detection (OD) models can be used to semantically understand what those features in the environment represent or mean.
For a long time, SLAM and OD have been taken as separate processes or tasks and research has been carried out on separate datasets. There are many datasets with object class and location labels and ground truth in the form of bounding boxes or segmentation masks. While there are many other datasets with maps and devices that pose ground truth. But very few datasets, if any include both, and the ones that do are mostly synthetic datasets.
At Ericsson, we are investigating how to perform joint detection and SLAM in a way that such processes benefit from each other in an efficient and integrated way. For that, we need a joint dataset that allows us to evaluate the performance of SLAM and object detection or segmentation algorithms together. However, semantically segmenting SLAM datasets can be very time-consuming and a fast and efficient acceleration via automatic annotation tools is needed.
Internship Assignment
The proposed project is an iterative process that will evolve and take shape during the internship work. It is expected to have many iterations and trial and error tests in the pipeline. The project will be carried out during the period (Sep-Dec) 2022. We are looking for an intern who can help us accelerate the semantic annotation of existing visual-inertial SLAM datasets in an iterative way, reducing manual annotation efforts. The internship can be converted into a master's thesis if necessary.
The scope of the internship includes but is not limited to:
a) Become acquainted to ML and SLAM frameworks
b) Become acquainted to object detection or segmentation frameworks using RGB images.
c) Carry out a literature survey on prior works related to automatic annotation processes
d) Develop python/OpenCV scripts and/or ROS wrapper/libraries for a proof of concept
Qualifications
We are looking for a self-motivated and creative student with the following qualifications:
Supervisor(s):
The internship coordinator and supervisor is Héctor Caltenco, who will be able to help with administrative matters, purchase of material and general internship-related issues, as well as take care of project matters and research-related questions.
Further Information:
If you have any questions about this position, please contact Nithesh Chandher Karthikeyan, Research Engineer at Department of Computing Science, Umeå University.
Thesis work in Software Engineering or Computer Security (15/30 ECTS)
In the Software Engineering and Security group, we aim to improve the quality of software by identifying and removing weaknesses during its whole life cycle – from the design and implementation phase to the deployment phase. We develop tools and approaches to analyze software, test software, understand the software and protect software from bugs and vulnerabilities. Our research has reald-world impact: Covid tests may leak personal data
Our research focuses on topics in software engineering and computer security such as:
Do you plan to do your bachelor/master thesis in software engineering or computer security?
Please, contact Prof. Alexandre Bartel: alexandre.bartel@cs.umu.se or 072 208 73 56.
Are you a fast learner and like to work with Robotics/AI (30 ECTS)
Intelligent Robotics Research Group develops formal and non-formal methods for intelligent robot behavior, such as visual perception or natural language usage. We are interested in algorithm development as well as interaction and design studies. Are you a fast learner, like to work in a team, have excellent communication skills, and would like to work in Robotics/AI?
More information
Please contact Thomas Hällström, Professor at the Department of Computing Science via e-mail for a discussion about a suitable thesis project (30 ECTS).
Machine learning has become very important in medical sciences as a means to automate for instance diagnosis and prognosis, and to personalise medicine and treatments. Machine learning is for instance used in medical imaging to automate parts of the radiation treatment planning after a cancer diagnosis.
We are curious about what exactly is learned about particular patients when building deep convolutional neural networks for medical imaging applications. Since models trained on patient data are commonly and freely shared online, there are potential privacy issues involved that are currently not fully understood. We are therefore looking for Master's thesis students who are interested in projects in this area.
For more information
If this sounds like something for you, please contact Tommy Löfstedt via e-mail: tommy@cs.umu.se for more information, on this and other potential projects.
The Responsible Artficial Intelligence Group are notably interested in studying the ethical and societal impact of AI and developing tools and methodologies to mitigate adverse effects; as well as agents that can better connect to humans and societies. We are open to a variety of proposals, from ethical assessments, to using our design methodologies and tools for concrete applications; from hard software for demonstrations to scientific activities.
As a specific example of a pursued track, we developed in house a cognitive component for replicating anxiety and its avoidance; and we wish to make a cool demo of it.
What if you would expand a video game with an anxious character? Or make a cool VR game for which the player can parametrize the stress level he/she will likely face instead of the "difficulty rating" of the game? What about helping us pushing the borders of science, and develop the next version of our anxiety-sensitive AI system?
For more information
If these are activities you may like, please contact Loïs Vanhée, Associate Professor at The Department of Computing Science via e-mail: lois.vanhee@umu.se
Deep Data Mining Group has been working on multimodal heterogeneous data analytics and data federation by applying the techniques of text mining, information retrieval, natural language processing, machine learning, and differential privacy. The main research topic are data driven and application-oriented such as entity-based social network analysis (e.g., sentiment, emotions, political view etc.), personal privacy analysis, and ontology-based knowledge graph construction etc. Additionally, the group has several connections to industry and academia who are interested to co-supervise master thesis projects, where we can conduct some interesting AI related interdisciplinary research (e.g., Covid pandemic event tracking and inference, medical screening, energy consumption and anomaly prediction, genetics&healthcare etc.). We welcome an open discussion regarding your thesis plan.
For more information
If you are interested, feel free to contact Lili Jiang, associate professor at the Department of Computing Science via e-mail: lili.jiang@cs.umu.se about a suitable master thesis project.
Autonomous Distributed Systems Lab: The group's main focus is new cloud/edge technologies with enhanced performance enabled by AI for the data processing required to support future hyper-distributed applications. Based on such the cloud/edge, we research data markets and data economy; therefore, we are currently looking for Master's students who want to do their thesis project (30hp) in our group. More specifically, we have several openings in:
a) Data markets allowing for data assets to be discoverable, efficiently and fairly priced, and shared/traded in a compliant way.
b) Scalable and AI-powered data management for data usability in different contexts, covering data provenance, data quality management, improving data interoperability, and metadata management.
For more information
Please contact Antonio Seo, eunil.seo@cs.umu.se, to discuss/define a thesis project.
When training deep neural networks in machine learning, the standard optimisation method is some variant of stochastic gradient descent. An hypothesis about why deep neural networks can generalise as well as they do, despite them typically having many more parameters than training data, is the utilisation of stochastic gradient descent. The hypothesis says that when using stochastic gradient descent, wide/flat minima are preferred over sharp/narrow ones, that stochastic gradient descent has the ability to "shake itself out" of sharp minima.
The goal of this project is to investigate different ways that stochastic gradient descent can work as a form of regulariser for the model, how it can avoid overfitting the training data. There are several different ways this can be tested, and the project would amount to select one or a few approaches, implement, and test them by comparing theoretical and practical results.
For more information
If this sounds like something for you, please contact Tommy Löfstedt, associate professor at the Department of Computing Science, through e-mail: tommy@cs.umu.se for more information on this, and other potential projects.
High-performance kernels for tensor computations (30 ECTS)
In a recent survey of software for tensor computations, we identified an enormous case of replication of efforts. Indeed, despite the importance and widespread use of tensors, a library of building blocks (i.e., the tensor counterpart of BLAS) is still missing. With this project we make a concrete proposal for a set of high-performance kernels to be used across disciplines in which computations involve tensors. Such disciplines range from machine learning and data science all the way to computational chemistry and computational mechanics.
The survey mentioned above: "The landscape of software for tensor computations".
For more information
Contact Paolo Bientinesi, pauldj@cs.umu.se, 0720 856 180 for more information.
MatchC: A C/C++ library for Pattern Matching
Mathematica (Wolfram) arguably offers the most powerful pattern matching capability of any programming language. A C/C++ counterpart, even if more limited in features and functionality, would be an extremely useful building block for a myriad of applications. This project aims at developing a high-performance pattern matching library, aiming for associative and/or commutative operators, with constraints, as well as single and multiple matches (akin to ./+/* in reg exps), and shared-memory parallelism.
In previous work, we developed MatchPy, a Python library for pattern matching. This constitutes the natural reference and starting point for this project. See also "Efficient Pattern Matching in Python".
For more information
Contact Paolo Bientinesi, pauldj@cs.umu.se, 0720 856 180 for more information.
Cybersecurity + Distributed/Tiny/Machine Learning + Computer Systems (15 ECTS/30 ECTS)
Autonomous Distributed Systems Lab - This wing of the Lab focuses on how to build adaptive, resilient, and responsible systems that operate in complex and real-world environments by investigating emerging and cutting-edge problems that remain to be addressed across domains, such as cloud-edge continuum, serverless computing, Internet of Things (IoT), and societal applications. Our research strives to find solutions that are low-cost, high-performance, reliable and unconditionally secure.
The primary goals of our research are intersections of cyber analytics and machine learning, but the areas are:
For more information
If you are ready or interested in writing your thesis (15/30 ECTS) in any of the above areas, then please don't hesitate to write a single email to Monowar Bhuyan, Assistant Professor, at e-mail: monowar@cs.umu.se.
Theses in Cloud Computing
Please see the Swedish page for a description (in Swedish)
Unveiling Duplication Patterns in Trouble Reports - Leveraging Machine Learning for Efficient Issue Tracking
In large systems such as the Radio Access Network, a continuous stream of tickets is generated whenever an issue occurs.
Many of these tickets may stem from the same root cause, resulting in numerous duplicate tickets that require analysis.
The objective of this thesis is to develop a machine learning (ML) algorithm that can learn from past tickets and determine whether a new ticket is likely a duplicate of a previous one.
Throughout this thesis, you will collaborate closely with other AI/ML experts to devise a solution aimed at enhancing the current ticket service tool
For more informtion contact Tobias Sundqvist tobias.sundqvist@tietoevry.com
+46 70 323 64 15
Join our project to help omics research (30hp)
Join our project to help omics research. We are building a comprehensive genome knowledge database to unlock the potential of genomic, proteomic and metabolomic data and enable discoveries to optimize our cell line development platform.
Our project objectives include integrating diverse genomic data sources, internal and publicly available; building a database; creating a robust update management. Students will gain practical database management experience, expand their knowledge, and benefit from industry networking opportunities.
The ideal start date is September 2023 but the project timeline is flexible to accommodate students' academic commitments. The timeline compromises a 10-12 week course work followed by a thesis course.
Contact Shanti Pijeaud for more information shanti.pijeaud@sartorius.com
Benefits and limitations of moving to Microsoft Orleans from a monolithic system grid computing system (30hp)
There are many challenges with developing complex, low-latency distributed systems: scalability, concurrency, reliability, performance and fault tolerance. The virtual actor model claims to help with all these challenges.
The goal of this project is to investigate the benefits and limitations of moving from a monolithic system with features of grid computing to a system using Microsoft Orleans.
A proof of concept will be implemented based on the real-life challenges of a high-throughput, complex distributed system with millions of updates every minute, and thousands of clients worldwide.
If this sounds interesting, please contact Simon Renström, founder of RebelBetting, at simon@rebelbetting.com.
Image processing and deep learning on tomography data of phosphorus-rich ash
The transition to a sustainable society requires efficient use of materials and energy, where recycling nutrients is an important part. Phosphorus is an important nutrient that today is only recycled in small amounts. This could be improved by resource recovery from residual streams such as ash or biochar formed during the thermal conversion of societal waste streams. The overall aim of our research is to improve knowledge of the ash's chemical and physical properties such as microstructure, porosity, and surface area and to study how these can affect the availability of nutrients that can be used as fertiliser.
The project therefore aims to develop tools for analysing tomography data with so-called deep learning. Computed tomography provides a 3D image of a sample and with the help of advanced image processing, detailed characteristics of the sample can be obtained. For this, deep machine learning can play an important role in streamlining and improving performance in data evaluation. The goal of the project is to develop interactive segmentation methods, and evaluate their performance, by comparing to manual segmentation and applicability to different samples.
For more information
If this sounds interesting, contact Anna Strangberg, from the Department of Applied Physics and Electronics, through e-mail: anna.strandberg@umu.se for more information.
Use of machine learning methods to identify patterns of seedling response to drought treatment with help of time-lapse images
The goal of the proposed project is to apply ML tools to learn predictive patterns of response to drought in Scots pine seedlings exposed to drought conditions with use of time-lapse images. Machine learning (ML) methods such as deep learning, elastic net and lasso have been tested to train models for organisms and object recognition. The focus within this project is to train ML models to identify patterns of Scots pine seedling change in responses to drought. Model training will be done on images obtained by time-lapse imaging during the process of drought treatment.
For more information
If this sounds interesting, contact Rosario Garcia Gil, from the Department of Plant Physiology, through e-mail: m.rosario.garcia@slu.se for more information.
Training machine learning methods to interpret genetic effects on wood properties
Development of cost-efficient DNA sequencing technology offers the possibility to generate genome data for thousands of samples, which opens the possibility to apply machine learning methods to predict relationships between an organism’s genotype and phenotype. A recent study on yeast, rice, and weat showed that almost all standard machine learning methods performed better than methods from classical statistical genomics. The goal of the proposed project is to develop an efficient machine learning method to learn predictive relationships between the exome’s genome data (genetic DNA information) and spruce growth and wood properties.
For more information
If this sounds interesting, contact Rosario Garcia Gil, from the Department of Plant Physiology, through e-mail: m.rosario.garcia@slu.se for more information.
Do you want to work interdisciplinary with medical technology at Region Västerbotten? (15or 30 ECTS)
Our mission is to conduct research and development in close collaboration with medical researchers. Our expertise in biomedical engineering provides hospital personnel with technical support in different research and development projects. Thesis has always been an important part of the activities of the dept. This uses the student’s theoretical knowledge in practice in a hospital setting and at the same time the student gets to know a future employer. The student helps us with tasks and to find new technical solutions to the challenges of our research.
Interested?
Please contact Helena Grip, Hospital engineer, assoc. prof. in biomedical engineering and adjunct university lecturer. Research and development within medical technology and movement analysis, email: helena.grip@regionvasterbotten.se or telephone: 090-785 40 29
Evity is a startup in Umeå that develops an HR tool for small and medium-sized companies. We use automation and form tools to create KPIs and processes in the tool. Now, we are looking for a student who wants to work in .net within Azure where we can find a collaboration to develop new features or improve existing ones. Size of project 15 or 30 ECTS.
We are an international team as our co-founder and CTO is from New Zealand, where he also works from. You can read more about us on www.evity.hr
Interested?
Please contact CEO, Tommy Eriksson, telephone: 070-696 70 22 or email: tommy@evity.hr
NONPI Medical develops and implements software tools for medical image analysis. The proposed project focuses on deep learning methods for medical imaging, e.g. tumor segmentation, super-resolution, or distortion correction.
There are many different frameworks available for implementing deep learning models, which is good for development and research, but also divides the field and published code into fractions, depending on the particular framework that was used. The result is that the field is divided, and the results are difficult to reproduce between framework users. An open source standard for machine learning models has been established (ONNX), however this format is yet to be commonly deployed, and especially so in medical imaging research and development.
To help spread developed models, and the ONNX format, we are currently looking for:
(a) master's thesis student(s) (30 p) who want to establish an open source collection of current state-of-the-art deep learning models and methods for medical imaging, mainly challenge winners, state-of-the-art models, and models from well-cited research papers. The project involves collecting and categorising pre-trained models, converting them from a diverse set of formats to the ONNX format, and testing them on medical image data in MICE Toolkit, a software for medical imaging developed by NONPI Medical AB.
For more information
If you are interested in learning about the state-of-the-art deep learning models and unifying the research community, please contact attila@nonpimedical.com
NONPI Medical AB develops and implements software tools for medical image analysis. The proposed project focuses on developing tools to design, alter, and train deep learning models and full machine learning pipelines for medical image processing, such as tumour segmentation, super resolution, or distortion correction.
There is much interest within for instance radiation therapy to use deep learning models for automation, but many in such fields lack the background to make substantial changes in existing models, and to develop their own specific models programmatically. With this background, NONPI Medical AB has developed a graphical tool to define the architecture for deep learning models. The existing tool is built on top of Python, which adds an extra layer of complexity since the rest of the program is written in C#, and also has problems whenever the underlying deep learning framework is updated.
The goal of this project it to evaluate different approaches to build deep learning models modularly in C#, and to find efficient graph algorithms to execute different steps of the machine learning pipeline on available hardware resources.
The goal of these developments is to aid the adaptation of existing deep learning models, and to make it possible for more researchers and medical practitioners to develop their own and be able to change or improve existing models and the entire maching learning pipeline. NONPI Medical AB is therefore looking for (a) master's thesis student(s) (30 hp) that wants to develop such tools for use within medical image analysis.
For more information
If you are interested in learning more about the project or how we use state-of-the-art deep learning models, please contact: attila@nonpimedical.com
AI to understand large scale biological data
MIMS has with the help of Johan Henrikssons research group established a new lab to produce single-cell data. With this method it is possible measure thousands (even millions) of cells, one at a time. The enormous datasets enable us to take a new approach to understanding biology, where we use AI to break down the complex measurements into knowledge.
This comes with computational challenges, from "how can we use AI to find a compressed data representation?" to "how do we efficiently handle several terabytes of sparse data?". Like the particle accelerator at CERN, biology now requires us to solve advanced computer scientific problems before we can go back to the biological questions.
We welcome thesis workers on all levels, from those who want fairly isolated problems to those who dare holistic approaches. It is a good thing if you want to learn some biology. Python is currently our standard language but we also use R, Java and C/C++ when the situation calls for it.
For more information
Please contact Johan Henriksson, researcher at MIMS. See also our website at www.henlab.org