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Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry Classification Minimum Requirements: PhD in Chemistry or related area Strong coding (Python) and
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scientific programming in Python and the use of frameworks such as PyTorch or TensorFlow. It is also necessary to have experience in federated learning and deep learning applied to medical imaging, as
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science related discipline. You will possess established expertise in data analysis including statistical programming (e.g., STATA, RStudio, Python). You will demonstrate previous experience of contributing
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photometry and/or time-series analysis. Research experience in observational astronomy, demonstrated through publications appropriate to your career stage. Solid programming skills (e.g., Python) and
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energy-system modelling, mathematical optimization, control, digital twins, or related areas. Strong programming and numerical modelling skills using Python, MATLAB/Simulink, Julia, hardware in the loop
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. Integration of physiologic waveform features with clinical metadata and outcomes. Creation of reproducible Python and/or MATLAB pipelines for clinical signal processing and analysis. Translation
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or equivalent approaches. Automated experimental testing, instrument control or data acquisition using tools such as LabVIEW, Python or MATLAB. Pre-employment checks and declarations Your employment is
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statistical software (R, Python, SAS, or similar); English proficiency for reading, writing, and presenting; full-time residence in Piracicaba. Desirable requirements Experience with large datasets and metadata
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publications in the field. Required skills include operation and alignment of custom laser instrumentation, and programming experience in e.g. MATLAB/Python/LabVIEW for data analysis and software experimental
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bioinformatics, particularly single‑cell RNA‑seq, multi‑omics, and spatial transcriptomics analysis. Specific Requirements Proficiency in Python and R; familiarity with common bioinformatics tools and pipelines