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PhaseV (HQ in Boston, R&D in Tel Aviv) is a cutting-edge startup, pushing innovation in the drug development process to bring new treatments to more patients, in a more efficient and precise way. Leveraging the power of advanced causal inference and pushing the boundaries of machine learning, PhaseV detects hidden signals in clinical data and extracts actionable insights for planning the optimal trial. The company's proprietary ML-based platform for adaptive clinical trial design and closed-loop execution enables sponsors to easily and quickly unlock the potential of adaptive clinical trials, reveal the optimal adaptive design to meet their trial objectives and execute more efficient, more precise, and more successful clinical trials. Collaborating with 7 of the top pharmaceutical companies, as well as multiple CROs and biotechs, we are poised to make a significant impact on how drugs are developed and brought to market.
PhaseV (HQ in Boston, R&D in Tel Aviv) is a cutting-edge startup, pushing innovation in the drug development process to bring new treatments to more patients, in a more efficient and precise way. Leveraging the power of advanced causal inference and pushing the boundaries of machine learning, PhaseV detects hidden signals in clinical data and extracts actionable insights for planning the optimal trial. The company's proprietary ML-based platform for adaptive clinical trial design and closed-loop execution enables sponsors to easily and quickly unlock the potential of adaptive clinical trials, reveal the optimal adaptive design to meet their trial objectives and execute more efficient, more precise, and more successful clinical trials. Collaborating with 7 of the top pharmaceutical companies, as well as multiple CROs and biotechs, we are poised to make a significant impact on how drugs are developed and brought to market.
We are seeking a talented Data Scientist to join our team of researchers. The ideal candidate will have a strong foundation in machine learning and statistics with an interest in causal inference. A couple of recent preprints we’ve released to get a sense of the challenges:
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Causal Responder Detection (https://arxiv.org/abs/2406.17571)
Robust CATE Estimation Using Novel Ensemble Methods. (https://arxiv.org/abs/2407.03690)
Optimizes clinical trial design and execution using causal AI.
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