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already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks
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; machine learning methods (i.e. supervised and unsupervised learning, deep learning, reinforcement learning, etc.); artificial intelligence methods (e.g., predictive modeling, natural language processing
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Postdoctoral Positions in PFAS Analytics, Degradation, and Thermophysical Properties - DTU Chemistry
thermophysical properties vary across the diverse PFAS chemical space and how these properties may be predicted using computational models. These positions offer an excellent opportunity for early‑career
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modeling – including predictive models for Alzheimer’s disease with a particular focus on sex-specific (female) risk. The harmonization pipeline will integrate female-specific and cognitive variables/items
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understanding of ML approaches for classification, anomaly detection, and prediction using high-frequency data. Experience with multilevel longitudinal data, missing data strategies, and clinical outcome modeling
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applied in particular to the modeling of 3D-printed concrete at the Navier laboratory, to better predict complex phenomena such as material curing and crack formation. Where to apply E-mail jeremy.bleyer
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, aimed at uncovering the key traits that define successful microbial biofertilizers, and to develop predictive models that can guide the rational design of next-generation BioAg products tailored
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or predictive modelling, edge AI, AI for biomaterials formulation, processing and manufacturing optimization. Wearable devices – wearable physiological sensors, smart textiles, soft robotics, and exoskeletons
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, genomic selection modeling, bioinformatics, proficiency in R, Python and/or Linux command line • Experience with DNA/RNA extraction and library preparation • Experience working with large-scale datasets
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, as well as from industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer