• Develop mathematical models for food shelf-life deterioration and failure.

• Model microbial growth, spoilage and inactivation.

• Model chemical and biochemical deterioration including oxidation, enzymatic reactions, pigment degradation and nutrient loss.

• Model moisture migration, mass transfer, packaging interactions and structural deterioration.

• Develop remaining shelf-life models under dynamic storage and cold-chain conditions.

• Define critical quality attributes, failure thresholds and shelf-life limiting mechanisms.

• Build time-to-failure and competing-failure models.

• Estimate model parameters from experimental, industrial and published datasets.

• Apply nonlinear regression, maximum likelihood and Bayesian parameter-estimation methods where appropriate.

• Quantify prediction uncertainty using confidence intervals, prediction intervals, Monte Carlo simulation and other uncertainty-propagation methods.

• Perform sensitivity, identifiability and robustness analyses.

• Model the quantitative effect of shelf-life interventions on deterioration pathways and expected shelf-life extension.

• Develop coupled models involving microbiology, chemistry, packaging, temperature and other interacting mechanisms.

• Validate models against independent datasets and define their domains of applicability.

• Establish scientifically justified model limitations and extrapolation boundaries.

• Translate mathematical models into formal computational specifications for Shelfex.AI.

• Work with food scientists, application engineers and software engineers to deploy validated models into industrial workflows.

• Develop reusable parameter libraries and model documentation.

• Contribute to scientific publications, technical reports and the continuing development of the HSLE framework.

Scientific Scope

Typical modeling problems may include:

• microbial growth and spoilage kinetics;

• predictive microbiology;

• oxidation and antioxidant systems;

• reaction kinetics;

• Arrhenius and Q10 temperature dependence;

• moisture and gas transfer;

• packaging permeability;

• modified-atmosphere systems;

• texture and structural deterioration;

• sensory-quality loss;

• accelerated shelf-life testing;

• dynamic temperature histories;

• multi-mechanism and competing shelf-life failure.

The scientist should be comfortable selecting and developing models such as:

• zero- and first-order kinetic models;

• Arrhenius models;

• Baranyi, Gompertz and logistic models;

• Weibull models;

• diffusion and mass-transfer models;

• probabilistic failure models;

• differential-equation systems;

• Bayesian models;

• coupled mechanistic models.

Remaining Shelf-Life Modeling

A central responsibility of this role is to move beyond assigning a fixed shelf-life value and develop state-dependent remaining shelf-life predictions.

Models may incorporate:

• current product state;

• temperature history;

• pH;

• water activity;

• oxygen and carbon dioxide;

• relative humidity;

• packaging properties;

• initial microbial load;

• formulation;

• processing history;

• intervention dose;

• storage conditions.

The objective is to determine not only how long a food normally lasts, but:

What state is the food currently in, what mechanism will cause failure, when will that failure occur, and how uncertain is that prediction?

Intervention Modeling

The scientist will also quantify the effects of shelf-life interventions such as:

• biological antimicrobial systems;

• protective cultures;

• natural-origin preservation systems;

• antioxidants;

• coatings;

• packaging technologies;

• modified atmosphere;

• oxygen and moisture control;

• process interventions;

• temperature-management strategies.

The expected modeling logic is:

Baseline state → Intervention → Parameter change → Failure trajectory → Expected shelf-life change

Interventions should therefore be represented quantitatively rather than only as qualitative recommendations.

Model Validation and Scientific Governance

The successful candidate must be able to distinguish clearly between:

• measured parameters;

• estimated parameters;

• literature-derived priors;

• assumptions;

• uncertain parameters;

• validated and unvalidated predictions.

• Develop mathematical models for food shelf-life deterioration and failure.

• Model microbial growth, spoilage and inactivation.

• Model chemical and biochemical deterioration including oxidation, enzymatic reactions, pigment degradation and nutrient loss.

• Model moisture migration, mass transfer, packaging interactions and structural deterioration.

• Develop remaining shelf-life models under dynamic storage and cold-chain conditions.

• Define critical quality attributes, failure thresholds and shelf-life limiting mechanisms.

• Build time-to-failure and competing-failure models.

• Estimate model parameters from experimental, industrial and published datasets.

• Apply nonlinear regression, maximum likelihood and Bayesian parameter-estimation methods where appropriate.

• Quantify prediction uncertainty using confidence intervals, prediction intervals, Monte Carlo simulation and other uncertainty-propagation methods.

• Perform sensitivity, identifiability and robustness analyses.

• Model the quantitative effect of shelf-life interventions on deterioration pathways and expected shelf-life extension.

• Develop coupled models involving microbiology, chemistry, packaging, temperature and other interacting mechanisms.

• Validate models against independent datasets and define their domains of applicability.

• Establish scientifically justified model limitations and extrapolation boundaries.

• Translate mathematical models into formal computational specifications for Shelfex.AI.

• Work with food scientists, application engineers and software engineers to deploy validated models into industrial workflows.

• Develop reusable parameter libraries and model documentation.

• Contribute to scientific publications, technical reports and the continuing development of the HSLE framework.

Scientific Scope

Typical modeling problems may include:

• microbial growth and spoilage kinetics;

• predictive microbiology;

• oxidation and antioxidant systems;

• reaction kinetics;

• Arrhenius and Q10 temperature dependence;

• moisture and gas transfer;

• packaging permeability;

• modified-atmosphere systems;

• texture and structural deterioration;

• sensory-quality loss;

• accelerated shelf-life testing;

• dynamic temperature histories;

• multi-mechanism and competing shelf-life failure.

The scientist should be comfortable selecting and developing models such as:

• zero- and first-order kinetic models;

• Arrhenius models;

• Baranyi, Gompertz and logistic models;

• Weibull models;

• diffusion and mass-transfer models;

• probabilistic failure models;

• differential-equation systems;

• Bayesian models;

• coupled mechanistic models.

Remaining Shelf-Life Modeling

A central responsibility of this role is to move beyond assigning a fixed shelf-life value and develop state-dependent remaining shelf-life predictions.

Models may incorporate:

• current product state;

• temperature history;

• pH;

• water activity;

• oxygen and carbon dioxide;

• relative humidity;

• packaging properties;

• initial microbial load;

• formulation;

• processing history;

• intervention dose;

• storage conditions.

The objective is to determine not only how long a food normally lasts, but:

What state is the food currently in, what mechanism will cause failure, when will that failure occur, and how uncertain is that prediction?

Intervention Modeling

The scientist will also quantify the effects of shelf-life interventions such as:

• biological antimicrobial systems;

• protective cultures;

• natural-origin preservation systems;

• antioxidants;

• coatings;

• packaging technologies;

• modified atmosphere;

• oxygen and moisture control;

• process interventions;

• temperature-management strategies.

The expected modeling logic is:

Baseline state → Intervention → Parameter change → Failure trajectory → Expected shelf-life change

Interventions should therefore be represented quantitatively rather than only as qualitative recommendations.

Model Validation and Scientific Governance

The successful candidate must be able to distinguish clearly between:

• measured parameters;

• estimated parameters;

• literature-derived priors;

• assumptions;

• uncertain parameters;

• validated and unvalidated predictions.

Models must be reproducible, traceable and scientifically testable.

The scientist will be expected to answer three questions for every important model:

1. Why should this model work?

2. What evidence supports it?

3. Under what conditions should it not be used?

Models must be reproducible, traceable and scientifically testable.

The scientist will be expected to answer three questions for every important model:

1. Why should this model work?

2. What evidence supports it?

3. Under what conditions should it not be used?

• Develop mathematical models for food shelf-life deterioration and failure.

• Model microbial growth, spoilage and inactivation.

• Model chemical and biochemical deterioration including oxidation, enzymatic reactions, pigment degradation and nutrient loss.

• Model moisture migration, mass transfer, packaging interactions and structural deterioration.

• Develop remaining shelf-life models under dynamic storage and cold-chain conditions.

• Define critical quality attributes, failure thresholds and shelf-life limiting mechanisms.

• Build time-to-failure and competing-failure models.

• Estimate model parameters from experimental, industrial and published datasets.

• Apply nonlinear regression, maximum likelihood and Bayesian parameter-estimation methods where appropriate.

• Quantify prediction uncertainty using confidence intervals, prediction intervals, Monte Carlo simulation and other uncertainty-propagation methods.

• Perform sensitivity, identifiability and robustness analyses.

• Model the quantitative effect of shelf-life interventions on deterioration pathways and expected shelf-life extension.

• Develop coupled models involving microbiology, chemistry, packaging, temperature and other interacting mechanisms.

• Validate models against independent datasets and define their domains of applicability.

• Establish scientifically justified model limitations and extrapolation boundaries.

• Translate mathematical models into formal computational specifications for Shelfex.AI.

• Work with food scientists, application engineers and software engineers to deploy validated models into industrial workflows.

• Develop reusable parameter libraries and model documentation.

• Contribute to scientific publications, technical reports and the continuing development of the HSLE framework.

Scientific Scope

Typical modeling problems may include:

• microbial growth and spoilage kinetics;

• predictive microbiology;

• oxidation and antioxidant systems;

• reaction kinetics;

• Arrhenius and Q10 temperature dependence;

• moisture and gas transfer;

• packaging permeability;

• modified-atmosphere systems;

• texture and structural deterioration;

• sensory-quality loss;

• accelerated shelf-life testing;

• dynamic temperature histories;

• multi-mechanism and competing shelf-life failure.

The scientist should be comfortable selecting and developing models such as:

• zero- and first-order kinetic models;

• Arrhenius models;

• Baranyi, Gompertz and logistic models;

• Weibull models;

• diffusion and mass-transfer models;

• probabilistic failure models;

• differential-equation systems;

• Bayesian models;

• coupled mechanistic models.

Remaining Shelf-Life Modeling

A central responsibility of this role is to move beyond assigning a fixed shelf-life value and develop state-dependent remaining shelf-life predictions.

Models may incorporate:

• current product state;

• temperature history;

• pH;

• water activity;

• oxygen and carbon dioxide;

• relative humidity;

• packaging properties;

• initial microbial load;

• formulation;

• processing history;

• intervention dose;

• storage conditions.

The objective is to determine not only how long a food normally lasts, but:

What state is the food currently in, what mechanism will cause failure, when will that failure occur, and how uncertain is that prediction?

Intervention Modeling

The scientist will also quantify the effects of shelf-life interventions such as:

• biological antimicrobial systems;

• protective cultures;

• natural-origin preservation systems;

• antioxidants;

• coatings;

• packaging technologies;

• modified atmosphere;

• oxygen and moisture control;

• process interventions;

• temperature-management strategies.

The expected modeling logic is:

Baseline state → Intervention → Parameter change → Failure trajectory → Expected shelf-life change

Interventions should therefore be represented quantitatively rather than only as qualitative recommendations.

Model Validation and Scientific Governance

The successful candidate must be able to distinguish clearly between:

• measured parameters;

• estimated parameters;

• literature-derived priors;

• assumptions;

• uncertain parameters;

• validated and unvalidated predictions.

Models must be reproducible, traceable and scientifically testable.

The scientist will be expected to answer three questions for every important model:

1. Why should this model work?

2. What evidence supports it?

3. Under what conditions should it not be used?

Requirements

• PhD or equivalent advanced research experience in Food Science, Food Engineering, Predictive Microbiology, Biochemical Engineering, Chemical Engineering, Biotechnology, Applied Mathematics, Statistics, Biological Systems Engineering, Process Engineering or a closely related quantitative discipline.

• Strong experience in mathematical or kinetic modeling.

• Demonstrated ability to translate physical, chemical or biological mechanisms into mathematical models.

• Experience with parameter estimation and model calibration.

• Strong statistical and quantitative reasoning.

• Understanding of uncertainty and prediction intervals.

• Ability to analyze complex experimental datasets independently.

• Strong scientific documentation and communication skills.

• Professional working proficiency in English.

Scientific Computing

Practical experience with at least one scientific programming environment is required.

Python is preferred.

Relevant experience may include:

• Python;

• R;

• MATLAB;

• NumPy;

• SciPy;

• pandas;

• statsmodels;

• PyMC;

• optimization algorithms;

• differential-equation solvers;

• scientific data visualization.

The successful candidate does not need to be a professional software engineer, but must be capable of independently implementing, testing and communicating scientific models.

Preferred Experience

Experience in several of the following areas would be particularly valuable:

• food shelf-life prediction;

• predictive microbiology;

• kinetic modeling;

• reaction kinetics;

• Bayesian statistics;

• Monte Carlo simulation;

• uncertainty quantification;

• accelerated shelf-life testing;

• microbial challenge studies;

• mass-transfer modeling;

• food-packaging modeling;

• digital twins;

• machine learning for food systems;

• industrial shelf-life validation;

• dynamic cold-chain modeling.

Experience combining academic scientific rigor with industrial food applications is particularly valued.

Candidate Profile

We are looking for a scientist who can move independently through the complete chain:

Food system → Failure mechanism → Mathematical model → Parameters → Uncertainty → Prediction → Validation → Computational implementation

The ideal candidate asks:

“What mechanism determines the shelf life of this food?”

before asking:

“Which model best fits the data?”

Position Mission

The mission of the Shelf-Life Modeling Scientist is:

To convert food shelf-life science into predictive, falsifiable and computable engineering models that can be deployed through Shelfex.AI and applied to real industrial food systems.

Why Join Handary

You will contribute to the development of a new engineering framework for food shelf life and work at the interface of food science, mathematical modeling and industrial implementation.

Your work will directly contribute to:

• the Handary Shelf-Life Engineering System;

• HSLE LAB;

• Shelfex.AI;

• predictive shelf-life models;

• industrial food applications;

• the next generation of quantitative shelf-life engineering.