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Postdoctoral In Bayesian Statistics Jobs in Illinois

EVOZYNE INC

Applied AI Research Fellow

Chicago, IL · On-site

We combine sequence-based models and variational autoencoders (VAEs) with Bayesian optimization ... Late-stage PhD student or postdoc in a quantitative or computational field * Hands-on experience ...

Varsity Tutors

Statistics Graduate Level Tutor

Normal, IL · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

Varsity Tutors

Statistics Graduate Level Tutor

Dekalb, IL · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

Varsity Tutors

Statistics Graduate Level Tutor

Wheaton, IL · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

Varsity Tutors

Statistics Graduate Level Tutor

Skokie, IL · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

Varsity Tutors

Statistics Graduate Level Tutor

Chicago, IL · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

MMS Holdings Inc

Study Design Statistician (US)

Chicago, IL · On-site

$120 - $170/hr

Bachelor's degree in mathematics, statistics, physics, pharmacology or a discipline with a strong ... Experience with Bayesian approaches to design and analysis of clinical data preferred. * Experience ...

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Postdoctoral In Bayesian Statistics information

What is a postdoctoral position in Bayesian statistics?

A Postdoctoral position in Bayesian Statistics is a research-focused role for individuals who have recently completed their PhD in statistics, mathematics, or a related field. These positions involve conducting advanced research using Bayesian methods, which apply probability to infer statistical conclusions. Postdocs often work on developing new Bayesian models, collaborating on interdisciplinary projects, and publishing research findings. Such positions are typically temporary and designed to further prepare researchers for academic, industry, or governmental roles.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in Bayesian statistics?

To thrive as a Postdoctoral Researcher in Bayesian Statistics, you need an advanced degree (typically a PhD) in statistics or a related field, with strong expertise in Bayesian inference and probabilistic modeling. Proficiency with statistical programming languages such as R, Python, or Stan, and experience with specialized Bayesian analysis software are highly valued. Excellent problem-solving skills, collaboration, and the ability to communicate complex statistical concepts clearly are standout soft skills for this role. These skills and qualities are crucial for conducting rigorous research, publishing impactful results, and contributing effectively to scientific teams.

What are some common challenges faced by postdoctoral researchers in Bayesian statistics, and how can they be addressed?

Postdoctoral researchers in Bayesian statistics often encounter challenges such as managing complex, high-dimensional data, staying current with rapidly evolving computational methods, and balancing independent research with collaborative projects. Effective strategies include leveraging open-source statistical software, actively participating in seminars and workshops to stay updated, and establishing regular communication with interdisciplinary teams. Building a strong professional network and seeking mentorship within the department can also help in navigating research obstacles and advancing one's career.

What is the difference between Postdoctoral In Bayesian Statistics vs Postdoctoral In Data Science?

AspectPostdoctoral In Bayesian StatisticsPostdoctoral In Data Science
Required CredentialsPhD in Statistics, Mathematics, or related fieldPhD in Computer Science, Statistics, or related field
Work EnvironmentAcademic research, university labsResearch institutions, tech companies, industry labs
Employer & Industry UsageUniversities, research institutesTech firms, finance, healthcare, consulting
Common Search & Comparison IntentSpecialized research roles in Bayesian methodsBroader data analysis and machine learning roles

Postdoctoral In Bayesian Statistics focuses on advanced research in Bayesian methods within academic settings, requiring deep statistical expertise. In contrast, Postdoctoral In Data Science covers a broader range of data analysis techniques, including machine learning, often in industry environments. Both roles require a PhD but differ in application focus and work environment.

What are popular job titles related to Postdoctoral In Bayesian Statistics jobs in Illinois?

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The top searched job categories for Postdoctoral In Bayesian Statistics jobs in Illinois are:

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Postdoctoral In Bayesian Statistics jobs near you

Applied AI Research Fellow

EVOZYNE INC

Chicago, IL • On-site

Full-time

Re-posted 24 days ago

Job description

Evozyne is one of the few AI-native biotech companies designing de novo therapeutic proteins and advancing them toward the clinic. Our teams apply AI to develop novel therapies within complex biological systems where data is imperfect, and discoveries have meaningful impact on patients’ lives. We are transforming how the industry approaches protein engineering.

Our platform, EvoGen, is both generative and predictive. Rather than focusing on structure alone, we build models that learn how protein sequence drives function, enabling the design of novel proteins optimized across multiple objectives, including potency, stability, specificity, and immunogenicity. Our model development is tightly integrated with proprietary experimental data, enabling rapid learning from biological reality. We combine sequence-based models and variational autoencoders (VAEs) with Bayesian optimization, using experimental data to rapidly design and refine proteins into impactful therapeutics.

The Applied AI Research Fellowship at Evozyne is designed for researchers who want to stress‑test ambitious ideas against one of the most challenging frontiers in applied AI today: generative design under real‑world biological constraints. As a Fellow, you will work on foundational questions in representation learning, generative modeling, and optimization, with the opportunity to see your ideas evaluated against real experimental outcomes and translated into therapeutic programs.

Your work will directly influence how Evozyne evaluates, evolves, and deploys its generative AI models for protein design.

Who You Are

You’re excited by problems where the data is messy, the constraints are real, and the path forward isn’t obvious. You thrive in ambiguity, and you’re motivated by applying your work to real-world scientific challenges to see how your ideas hold up in practice. You are already operating at the leading edge of applied AI and want to push your thinking further by applying it to complex, high-impact challenges in drug discovery.

What You’ll Be Investigating

As an Applied AI Research Fellow, you will help drive the evolution of Evozyne’s generative AI design platform. Example research areas include:

  • Benchmarking generative protein models, including Evozyne’s own, on their ability to produce functionally diverse and biologically meaningful designs.
  • Evaluating the value of integrating large-scale metagenomic resources (e.g., Global Ocean Gene Catalog) into current internal database.
  • Exploring alternatives and extensions to Bayesian Optimization such as Knowledge Gradient, Entropy Search, and related methods for multi-objective optimization problems.
  • Developing and applying deep learning approaches for remote homology detection
  • Investigating multi-family VAE models to enable protein design when sequence support is limited or when optimizing phenotypes across protein families.

These efforts are intended to surface failure modes, challenge assumptions, and directly inform how Evozyne designs proteins and advances therapies.

Education + Experience

  • Late-stage PhD student or postdoc in a quantitative or computational field
  • Hands-on experience applying AI/ML to complex, real-world or scientific datasets
  • Experience working on problems where data is noisy, incomplete, or difficult to interpret
  • Familiarity with modern machine learning approaches (e.g., deep learning, generative models, or related methods)
  • Evidence of meaningful contribution to research, open-source work, or applied projects
  • Exposure to interdisciplinary work (e.g., biology, chemistry, physics, or other scientific domains) is a plus

Why Evozyne

Few places offer the combination of proprietary experimental data, real therapeutic programs, and the freedom to explore foundational AI questions under real biological constraints. If you want your best ideas tested where they matter most, and the chance to help redefine how AI is applied to protein design, we’d like to connect.