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Artifact Repository: Artifactory Network: SSH, SCP, FTP, HTTP(S), TCP/IP, STIG compliance, firewall/iptables setup. Scripting: Bash, Perl, Powershell, YAML, Python Operating System: LINUX (Redhat, Ubuntu
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developability assessment tools such as aggrescan, AbImmPred etc. - Strong knowledge of different machine learning architectures and training techniques is essential. - Proficiency with programming in Python and
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Python or C/C++. The candidate should have an interest in developing novel bivariate methods in machine learning for molecular property prediction within an interdisciplinary application. Ideally
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network optimization tools. Knowledge, Skills, and Abilities Strong knowledge of automation frameworks and scripting (e.g., Python, Bash, PowerShell). Hands-on experience with monitoring tools like
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well as pediatric malignancy. The successful candidate will have experience analyzing data, and proficiency in standard statistical software, including SPSS as well as R. Knowledge of python is a plus. If the
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applied statistics, data science, machine learning, text analysis, and familiarity with coding in R and/or Python. All applications must be submitted through Columbia University?s Academic Search and
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accepted for publication. Expected skills Proficiency in programming (R, Fortran, Python) and the ability to learn new programming languages, develop code, and analyze data. Knowledge of GIS tools and
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Studies on Air Pollution and Health group (https://www.hsph.harvard.edu/nsaph/). The position will be under the supervision of Principal Research Scientist, Dr. Danielle Braun, in Biostatistics. Job
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Independent way of working Basic programming skills (Python) advantageous Very good knowledge of German or English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally
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SQL Server and experience with Python are an asset Criminal record check will be required Department: Info & Communications Tech, Enterprise Arch & Data Management Status: Term CSPIP 16 month internship