Post Doctoral Associate

%3Cp class=%22MsoNormal%22 style=%22line-height:150%;margin-bottom:0in;text-align:justify;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-font-kerning:0pt;mso-ligatures:none;%22%3E%3Cstrong%3EPostdoctoral Researcher in AI-Driven Pharmacometrics and PK/PD Modeling%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp class=%22MsoNormal%22 style=%22line-height:150%;margin-bottom:0in;text-align:justify;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-font-kerning:0pt;mso-ligatures:none;%22%3EApplications are invited for a postdoctoral researcher position in the laboratory of Dr. Junmei Wang in the Department of Pharmaceutical Sciences at the University of Pittsburgh. Dr. Wang leads an interdisciplinary research program at the University of Pittsburgh School of Pharmacy focused on developing next-generation computational methods to advance rational drug discovery in the era of artificial intelligence. The laboratory develops and applies machine learning and AI-driven approaches to address challenging problems in pharmacometrics, ADME/PK/PD modeling and simulation, and computational systems pharmacology, with the goal of supporting translational and quantitative drug development. The research environment is highly collaborative and provides trainees with unique opportunities to work at the intersection of AI-enabled drug discovery, ADME- and PK/PD-guided drug development, and precision medicine%3C/span%3E%3C/span%3E%3C/p%3E%3Cp class=%22MsoNormal%22 style=%22line-height:150%;margin-bottom:0in;mso-outline-level:3;text-align:justify;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-font-kerning:0pt;mso-ligatures:none;%22%3E%3Cstrong%3EPosition Summary%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp class=%22MsoNormal%22 style=%22line-height:150%;margin-bottom:0in;text-align:justify;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3EThe postdoctoral associate will work at the intersection of clinical data science, machine learning, and quantitative %3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-font-kerning:0pt;mso-ligatures:none;%22%3Epharmacology%3C/span%3E. The primary focus includes PBPK modeling, population PK/PD modeling, and the development of machine learning-based approaches for pharmacokinetic modeling. The role emphasizes applying AI and deep learning methods to improve drug metabolism prediction, support virtual screening workflows, and enable data-driven drug discovery using large-scale clinical and biochemical datasets.%3C/span%3E%3C/p%3E%3Cp class=%22MsoNormal%22 style=%22line-height:150%;margin-bottom:0in;mso-outline-level:3;text-align:justify;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-font-kerning:0pt;mso-ligatures:none;%22%3E%3Cstrong%3EKey Responsibilities%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp class=%22MsoListParagraphCxSpFirst%22 style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-add-space:auto;mso-list:l1 level1 lfo1;mso-outline-level:3;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3EDesign, implement, and validate deep learning models (e.g., GNNs, LSTMs, and multi-task learning frameworks) for predicting drug metabolism-related properties%3C/span%3E%3C/p%3E%3Cp class=%22MsoListParagraphCxSpMiddle%22 style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-add-space:auto;mso-list:l1 level1 lfo1;mso-outline-level:3;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3EDevelop, evaluate, and scale PBPK and population PK/PD models to optimize dosing strategies across special populations, including pediatric and clinical subgroups%3C/span%3E%3C/p%3E%3Cp class=%22MsoListParagraphCxSpMiddle%22 style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-add-space:auto;mso-list:l1 level1 lfo1;mso-outline-level:3;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3ECurate, clean, and preprocess large-scale heterogeneous biomedical datasets%3C/span%3E%3C/p%3E%3Cp class=%22MsoListParagraphCxSpLast%22 style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-add-space:auto;mso-list:l1 level1 lfo1;mso-outline-level:3;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3ECollaborate with experimental scientists, clinical researchers, and external stakeholders; analyze and interpret results; publish in peer-reviewed journals and present at national/international conferences%3C/span%3E%3C/p%3E%3Cp class=%22MsoNormal%22 style=%22line-height:150%;margin-bottom:0in;mso-outline-level:3;text-align:justify;text-justify:inter-ideograph;%22%3E%3Cspan style=%22font-family:%26quot;Times New Roman%26quot;,serif;%22%3E%3Cspan style=%22mso-fareast-font-family:%26quot;Times New Roman%26quot;;mso-font-kerning:0pt;mso-ligatures:none;%22%3E%3Cstrong%3EQualifications%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp; %3C/span%3EPhD in pharmaceutical sciences, pharmacometrics, computational biology, biomedical informatics, computer science, or a closely related quantitative discipline%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3EStrong background in PBPK modeling, population PK, or PK/PD modeling; hands-on experience with NONMEM, Simcyp, Monolix, or MATLAB is highly desirable%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3EExperience developing machine learning models for the prediction of drug metabolism-related properties%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp;%26nbsp; %3C/span%3EStrong programming skills in Python and/or R; experience with Linux environments%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%3Cspan style=%22mso-list:Ignore;%22%3E•%3C/span%3E%3Cspan style=%22font:7.0pt %26quot;Times New Roman%26quot;;mso-list:Ignore;%22%3E%26nbsp;%26nbsp;%26nbsp; %3C/span%3EStrong interdisciplinary communication and project management skills, with ability to work independently and collaboratively in a multidisciplinary environment.%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3E%26nbsp;%3C/p%3E%3Cp style=%22line-height:150%;margin:0in 0in 0in 45.0pt;mso-list:l0 level1 lfo2;text-align:justify;text-indent:-.25in;text-justify:inter-ideograph;%22%3ERequired Documentation: CV, cover letter and the contact information for 2 references. Qualified applicants should apply at www.join.pitt.edu, requisition number 26003284.%26nbsp;%3C/p%3E

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