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been oriented around high performance computing (HPC) but are increasingly migrating to cloud-based solutions. We are seeking a talented software engineer to bring in this transition. You'll Be Solving
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tools for High-Performance Computing (HPC) applications. Qualifications/Requirements Qualifications / Discipline: - PhD’s degree in Physics, Materials Science, Computer Science, Data Science, Artificial
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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
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(data aggregation and processing, database management, analysis, and visualisation). Operating within Unix-based environments (headless servers, HPC clusters), with a preference for experience managing
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cancer genomics and functional interpretation of genetic variants. Proficiency in Python, R, or other bioinformatics languages. Knowledge of cloud computing, and high-performance computing (HPC
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-image, OpenCV, Git, and Bash. Experience working with HPC clusters (e.g. SLURM) or with cloud technologies such as AWS, Azure, or GCP. Experience working with federated learning frameworks such as Flower
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. Desired qualifications: Experience from field work Experience with high performance computing (HPC) Experience with supervision of students All candidates and projects will have to undergo a check versus
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development (e.g., RNA-seq, single-cell RNA-seq, exomes). An expert level user of HPC infrastructure / Linux systems and Able to implement key R and / or Python packages. Track record of publications
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research experience. Experience in Chemoinformatics in a University, academic institute, or industrial setting. An expert level user of HPC infrastructure / Linux systems and Able to implement key R and / or
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, Scikit-image, OpenCV, Git, and Bash. Experience in developing and finetuning foundation models for biological applications. Experience working with HPC clusters (e.g. SLURM) or with cloud technologies