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: Implementation and fine-tuning of antibody design models (RFdiffusion and boltzgen, AlphaFold3 etc.). Implementation of affinity prediction and maturation (FoldX, RosettaFold, ESM etc.), virtual screening and
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large datasets in wheat, Develop and implement novel approaches for genome-wide predictions of complex traits. Your qualifications and skills: You hold a MSc in plant science, plant breeding, biology, or
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will use a large dataset of P. aeruginosa genomes and experimental metadata to predict key mutations to the organism. The postdoctoral researcher will join the Whelan lab led by Dr. Fiona Whelan
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, assess the health state of systems, and predict their future evolution and remaining useful life. The proposed approach integrates physics-based and data-driven modeling techniques, including machine
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test coupons used in HiSOPE RF/microwave PCB & interconnects: Layout controlled‑impedance CPW/microstrip transitions from drivers to OLED fixtures; model launch structures, vias, and ground‑reference
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‘centromeric’ view on ageing by: i) characterizing age-associated changes in centromere features in cellular models; ii) identifying the intrinsic (genetic and epigenetic) and extrinsic (viral infection
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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plus. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing
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learning (AI/ML) being a major focus. Many of the laboratory's interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict
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. The Postdoctoral Associate will work under the direction of the Principal Investigator in building a first-of-its-kind Software as a Medical Device (SaMD) that predicts, detects, and manages SSIs by fusing RGB