Translational Medicine Academy Fellow / Translational Medicine Academy Fellowess for Pharmacokinetics Sciences Modeling & Simulation

Novartis Pharma AG - May 6, 2026

Job ID

REQ-10077029

May 04, 2026

Switzerland

Summary

At Translational Medicine (TM), we are dedicated to delivering groundbreaking medicines to patients and bridging the gap between research and clinical application. TM plays a crucial role in translating scientific advances into new therapies, ultimately impacting patients worldwide.

During this intensive 2-year program, you will receive comprehensive training and mentorship in a diverse, multicultural, and inclusive environment. If you are ready to embark on a transformative journey and make a significant impact on the lives of millions, this opportunity is for you!

About the Role

This advert is specifically for Pharmacokinetics Sciences Modeling & Simulation within the TM Academy. Other TM Academy roles are advertised separately.

  • REQ-10076855 - TM Academy Clinical Science & Innovation (CS&I)
  • REQ-10077055 - TM Academy Preclinical Safety - Data Science
  • REQ-10077246 - TM Academy Fellow in Biomarker Development
  • REQ-10076845 - TM Academy Biomarker Development Laboratory Excellence and Operations (BMD LEO)

Location: Basel, Switzerland

Duration of program: 24 months

Program start: 01 September 2026

Apply online using the form below. Only applications matching the job profile will be considered.

Job Description

Your journey begins with a basecamp: a 4-week immersive blended experience designed to introduce you to our organization, the drug development process, the various departments in Translational Medicine, the clinical trial process, and the technical tools required for your work.

Throughout the program, fellows will engage in continuous learning focused on soft skills, cross-functional exposure, and thematic introduction series, broadening your perspective beyond immediate teams and functions.

As a TM Academy fellow in PKS Modeling & Simulation, you will leverage your quantitative modeling expertise to advance translational immunology. You will join our team in developing a mechanistic, quantitative framework for B cell-driven indications, employing state-of-the-art mechanistic modeling, including physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) methods.

Key Responsibilities

  • Develop and implement a physiology-based PK/PD modeling framework that links drug exposure and target engagement to B-cell and plasma cell differentiation, trafficking, and turnover kinetics.
  • Integrate translational evidence from in vitro, in vivo, and early clinical data to estimate parameters and quantify uncertainty related to immune-reset biomarkers.
  • Communicate results effectively through presentations, reports, and manuscripts while collaborating closely with immunology, translational, and modeling stakeholders.
  • Own an end-to-end, production-ready modeling platform, expanding expertise at the interface of quantitative pharmacology and immunology.
  • Experience the impact of translational modeling on drug development, informing dose and schedule optimization across various therapies and indications.

Role Requirements

Candidates should have:

  • A PhD in quantitative pharmacology, bioengineering, systems biology, applied mathematics, biostatistics, computational biology, or a related discipline.
  • Hands-on experience in building and evaluating mechanistic models (e.g., ODE/PDE, compartmental, systems pharmacology, PK/PD).
  • Programming proficiency in at least one scientific language (e.g., Python, R, MATLAB SimBiology) and the ability to write reproducible, modular, well-documented code.
  • Experience working with biological datasets (preclinical and/or clinical), including data cleaning, visualization, model fitting, and interpretation.
  • Strong communication and collaboration skills, with an ability to translate complex quantitative findings into clear biological and project implications.

Additional skills that would be a plus:

  • Background in immunology, particularly B-cell biology or immune cell trafficking.
  • Experience modeling biologics and/or cell therapies, including target engagement and cell-killing dynamics.
  • Familiarity with physiology-based or multi-compartment modeling of immune-cell dynamics in therapies such as CAR-T cell therapy.
  • Familiarity with PBPK concepts and software (e.g., mrgsolve, nlmixr2, NONMEM, Monolix, SimBiology, Stan).
  • A coding portfolio related to mechanistic modeling or systems biology.

Accessibility and Accommodation

Novartis is committed to working with and providing reasonable accommodations to all individuals. If you need a reasonable accommodation for any part of the recruitment process due to a medical condition or disability, please email diversity.inclusion_ch@novartis.com and include your request and contact information. Remember to mention the job requisition number in your message.

Why Novartis

At Novartis, helping people with diseases and their families takes more than innovative science. It requires a community of passionate individuals like you. Together, we collaborate, support, and inspire one another to achieve breakthroughs that change patients' lives. Are you ready to create a brighter future with us?

Learn more about our values and culture here.

Benefits and Rewards

Explore all the ways we’ll support your personal and professional growth. Please read our handbook (PDF 30 MB).

Division

Biomedical Research

Business Unit

Research

Location

Switzerland

Site

Basel (City)

Company / Legal Entity

C028 (FCRS = CH028) Novartis Pharma AG

Functional Area

Others

Job Type

Full-time

Employment Type

Early Career (Fixed Term)

Shift Work

No

Translational Medicine Academy Fellow for Pharmacokinetics Sciences Modeling & Simulation

Apply online using the form below. Only applications matching the job profile will be considered.

Location : BASEL
Country : Switzerland

Application Form

Please enter your information in the following form and attach your resume (CV)

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