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environmental data analysis. Familiarity with theoretical and practical aspects of scientific deep learning; Proficiency in programming languages such as Python and R. Strong experience working with climate and
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(hybrid) quantum computing, reflected in demonstrable outputs, for instance, by a relevant PhD thesis and/or publications. Proficiency in Python and quantum software development kits (e.g., Qiskit
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(Signal/channel generation, simulation environments, semantic compression, etc.), Semantic-aware Models and Explainable AI Proficiency in Python and machine learning frameworks such as PyTorch Hand-on
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the following documents: Application letter (free format); Copies of academic diplomas: Bachelor’s, Master’s; Relevant diplomas, awards, certificates; Curriculum Vitae (EUROPASS format, signed and dated); List
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to research, teaching, and departmental operations Requirements: • A completed doctoral degree in chemistry, bio technology or a related field. • Strong programming skills in Python, R, or similar languages
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provides solutions and analyses to support strategic and tactical decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. This list of duties and
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simulations of protoplanetary discs and planet formation in the context of core accretion paradigm. Proficient computational skills especially in Python, FORTRAN or C++. A good understanding of the chemistry of
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skills in at least one relevant language (e.g., Python, R, Rust, JavaScript) Familiarity with smart contract interaction (e.g., web3.py/ethers.js, Solidity/Vyper, sub-graph indexing), and local development
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in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings
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models), including experience with their application to biomedical and biological data; Experience with machine learning frameworks and programming languages (e.g. Python) for handling large-scale text and