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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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systems Conduct data collection, cleaning, quality assurance, and validation activities Develop models to analyze costs and performance of wastewater technologies in Python or similar format. Participate in
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multiple modalities to build robust AI systems is an advantage Interdisciplinary Applications: Leveraging LLM / VLMs for interdisciplinary problems, such as: AI-driven scientific discovery, automating
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(target gene selection, power analyses, guide-library design, readout selection). Build, maintain, and document reproducible analysis pipelines (Python/R; Snakemake/Nextflow preferred) for novel
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testing data. Knowledge of scientific software in python (numpy, scipy, pandas) or related data analysis suite. Record of productive and creative research as demonstrated by publications and conference
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generate a new understanding of MoSS. These advances will have applications across multiple sectors, including pharmaceuticals, agrochemicals, and food industries. There are multiple opportunities
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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the analysis of multimodal spatial omics data across multiple projects, collaborating closely with experimental biologists. Main tasks and responsibilities: Develop and implement robust computational pipelines
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, students, and external partners. Contribute to teaching, mentoring, and curriculum development. Knowledge, Skills, and Abilities Strong background in AI, GNC, and SDA. Demonstrated proficiency in C++, Python
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++, Python, MATLAB, and modern machine learning frameworks. Proven research record, including peer-reviewed publications. Excellent communication and collaboration skills. Minimum Qualifications Ph.D. in