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Bayesian Phd Jobs in Illinois (NOW HIRING)

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 ...

MMS

Study Design Statistician (US)

Chicago, IL · On-site

... PhD preferred. * 5-7 years of experience in the application of medical statistics (pharma, CRO ... Experience of Bayesian approaches to design and analysis of clinical data preferred. * Experience ...

Until

Machine Learning Engineer / Scientist

Mundelein, IL

$140K - $240K/yr

... bayesian optimization. * Track record of developing ML approaches for scientific discovery, as ... Preferred Qualifications * 3+ years of relevant professional or research experience, or a PhD in a ...

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Bayesian Phd information

What is a Bayesian PhD?

A Bayesian PhD typically refers to an individual who has completed a doctoral program with a focus on Bayesian statistics or Bayesian methods in their research. Bayesian statistics is a branch of statistics that uses probability distributions to represent uncertainty about unknowns, updating beliefs as new data becomes available. Students in this field learn to develop and apply Bayesian models to a wide range of problems in science, engineering, and social sciences. A PhD program with a Bayesian focus often involves advanced coursework in probability theory, statistical inference, and computational methods, as well as original research using Bayesian approaches.

What are the key skills and qualifications needed to thrive as a Bayesian PhD?

To thrive as a Bayesian PhD, you need advanced knowledge of probability theory, statistical inference, and mathematics, typically supported by a doctoral degree in statistics, mathematics, or a related field. Proficiency with statistical programming languages like R, Python, and specialized Bayesian tools such as Stan or BUGS is essential. Strong critical thinking, problem-solving, and clear communication skills help in articulating complex analyses and collaborating across disciplines. These capabilities are crucial for developing rigorous models, conducting impactful research, and translating statistical insights into actionable solutions.

What are some common challenges faced by a Bayesian PhD researcher during collaborative projects?

Bayesian PhD researchers often collaborate with interdisciplinary teams, which can present challenges such as communicating complex statistical concepts to non-specialists and integrating Bayesian methods with other analytical frameworks. Balancing the depth of theoretical work with practical problem-solving, managing computational demands, and aligning project goals with collaborators' expectations are also common hurdles. Successful collaboration typically requires strong communication skills, adaptability, and a willingness to bridge methodological gaps between disciplines.

What is the difference between Bayesian Phd vs Data Scientist?

AspectBayesian PhdData Scientist
Required CredentialsPhD in Statistics, Mathematics, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch-focused, academic or specialized industry rolesBusiness-focused, tech companies, or consulting firms
Industry UsageAcademic research, advanced analytics, specialized modelingData analysis, machine learning, business insights
Common Search/ComparisonYesYes

While a Bayesian PhD specializes in advanced statistical modeling and research, a Data Scientist applies data analysis and machine learning techniques in practical business contexts. Both roles require strong analytical skills, but the Bayesian PhD typically focuses on theoretical development, whereas the Data Scientist emphasizes application and implementation.

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The top searched job categories for Bayesian Phd jobs in Illinois are:

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Bayesian Phd jobs near you

Applied AI Research Fellow

EVOZYNE INC

Chicago, IL • On-site

Full-time

Re-posted 20 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.