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, preferably in Python Strong ability to work in teams Perseverance and ability to get things done A high motivation and commitment to excel in your work Willingness to travel, attendance of national and
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Python, and a genuine interest in supporting others while building scalable, well-designed analysis pipelines. This role is ideal for someone with a background in neuroscience or computational data science
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ensure models can be deployed in large data real-world scenarios. Strong programming skills in Python, PyTorch, and/ or JAX are required, along with the ability to reason about neural network behavior from
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auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application Application files should contain a resumé, an application letter and grade records of the 2 last
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experience (perception, prediction, planning/control integration) Proficiency in Python and modern ML tooling is expected, along with familiarity with a quantum SDK (e.g. Qiskit). 5 How To Apply Interested
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scientific programming and algorithms is desirable Experience working in a research environment a plus General languages (preferred): C++, Python, MATLAB CUDA (strongly preferred) Machine learning knowledge
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, NoSQL, ETL pipelines), programming languages such as Python, Linux, API development and system interoperability, data governance and compliance (e.g. GDPR, security standards). Experience working within a
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metabolic modelling, constraint-based modelling, systems biology, or integrative multi-omics analysis. Strong technical background in programming and data engineering (e.g. Python, Linux environments
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neuroscience, or related fields. Demonstrated hands-on experience with neural circuit-based approaches. Strong programming skills (Python preferred). Experience analyzing time-series data, preferably from
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(e.g. Python, Rust) and modern software development techniques (version control, CI/CD) Track record of scientific output and engagement with the computational genomics community Desirable Knowledge