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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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: Extensive experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and
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NextFlow is a plus. Programming experience is required (Python, R). Previous experience in Genomics data analysis Strong analytical, organizational, and record-keeping skills Interest in working in a
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skills in R and/or python Experience with iPSC and/or cell culturing What we offer As part of the VIB-UAntwerp Center for Molecular Neurology and VIB , the applicant will benefit from established
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operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing libraries (numpy, scipy, JAX…) and machine learning libraries
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bioinformatics, physics, statistics, computer science, computational biology, or related fields. Experience programming in Python (or R) as well as bash/shell scripting. Experience with machine-learning and deep
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. Demonstrated skills in Python programming, or other computer programming. Strong interest in data science, such as data collection and curation, modelling. Excellent written and oral English communication skills
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analysis, Python programming, and deep learning frameworks (e.g., TensorFlow, PyTorch) is expected. Prior work in cancer research or medical imaging is an asset. The successful candidate will join an
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developed python-based EM forward operator. Contributing to the development of a freeware software package that offers both forward and inverse modeling capabilities for FEM and TEM data. Collection of FEM