DoctorateFaculty of Science & Applied Sciences

PhD in Theoretical Cosmology and Particle Simulations

Welcome, future explorers of the cosmos. Our journey will not just be to learn physics, but to use the most powerful tools available to simulate and understand the very fabric of reality.

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Level: Doctorate
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

NXAcademic
Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Theoretical Cosmology, Particle Physics, Quantum Field Theory, High-Performance Computing

Practical focus

Computational Physics, Quantum Computing, Scientific Programming, High-Performance Computing

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

JPL (Jet Propulsion Lab)
Opportunities in planetary science data analysis

Career opportunities

Computational Physicist
In a research lab or tech company
Data Scientist
In fields requiring complex modeling
Aerospace Engineer
Working on propulsion or navigation systems
Research Scientist
In academia or a national lab

Jobs and projects

Problem-Solving
Tackling complex, multi-faceted problems
Programming
Proficiency in languages like Python or C++
Critical Thinking
Questioning fundamental assumptions
Communication
Explaining complex physics concepts clearly

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- You will learn to think like a builder, not just a theorist. I will give you the practical skills and confidence to not just study the universe, but to build your own digital version of it, one line of code at a time.
Skills you build
- Advanced Simulation: Designing and running complex physics simulations.
- Theoretical Modeling: Building abstract models of physical systems.
- Data Analysis: Interpreting data from large-scale simulations.
- High-Performance Computing: Using supercomputers for scientific research.

Listed courses

Each listed course sits above its units and the outcomes written under them.

PhD in Theoretical Cosmology and Particle Simulations

01Advanced Quantum Field Theory
1. FoundationsFoundations of Advanced Quantum Field Theory
  The learner can you will learn to think like a builder, not just a theorist, as applied to Advanced Quantum Field Theory.
  The learner can i will give you the practical skills and confidence to not just study the universe, but to build your own digital version of it, one line of code at a time, as applied to Advanced Quantum Field Theory.
2. MethodsMethods in Advanced Quantum Field Theory
  The learner can apply a method from Advanced Quantum Field Theory to a documented case.
  The learner can select an appropriate method from Advanced Quantum Field Theory for a stated problem.
3. ApplicationApplication of Advanced Quantum Field Theory
  The learner can evaluate a practice of Advanced Quantum Field Theory against a stated criterion.
  The learner can high-Performance Computing: Using supercomputers for scientific research, as applied to Advanced Quantum Field Theory.
02General Relativity and Black Hole Simulation
1. FoundationsFoundations of General Relativity and Black Hole Simulation
  The learner can explain the core terms of General Relativity and Black Hole Simulation.
  The learner can distinguish related ideas inside General Relativity and Black Hole Simulation.
2. MethodsMethods in General Relativity and Black Hole Simulation
  The learner can apply a method from General Relativity and Black Hole Simulation to a documented case.
  The learner can select an appropriate method from General Relativity and Black Hole Simulation for a stated problem.
3. ApplicationApplication of General Relativity and Black Hole Simulation
  The learner can evaluate a practice of General Relativity and Black Hole Simulation against a stated criterion.
  The learner can transfer General Relativity and Black Hole Simulation to a new documented context.
03Cosmology and the Large-Scale Structure of the Universe
1. FoundationsFoundations of Cosmology and the Large-Scale Structure of the Universe
  The learner can explain the core terms of Cosmology and the Large-Scale Structure of the Universe.
  The learner can distinguish related ideas inside Cosmology and the Large-Scale Structure of the Universe.
2. MethodsMethods in Cosmology and the Large-Scale Structure of the Universe
  The learner can apply a method from Cosmology and the Large-Scale Structure of the Universe to a documented case.
  The learner can select an appropriate method from Cosmology and the Large-Scale Structure of the Universe for a stated problem.
3. ApplicationApplication of Cosmology and the Large-Scale Structure of the Universe
  The learner can evaluate a practice of Cosmology and the Large-Scale Structure of the Universe against a stated criterion.
  The learner can transfer Cosmology and the Large-Scale Structure of the Universe to a new documented context.
04Numerical Methods in Physics
1. FoundationsFoundations of Numerical Methods in Physics
  The learner can explain the core terms of Numerical Methods in Physics.
  The learner can distinguish related ideas inside Numerical Methods in Physics.
2. MethodsMethods in Numerical Methods in Physics
  The learner can apply a method from Numerical Methods in Physics to a documented case.
  The learner can select an appropriate method from Numerical Methods in Physics for a stated problem.
3. ApplicationApplication of Numerical Methods in Physics
  The learner can evaluate a practice of Numerical Methods in Physics against a stated criterion.
  The learner can transfer Numerical Methods in Physics to a new documented context.
05Research Seminar in Theoretical Cosmology
1. FoundationsFoundations of Research Seminar in Theoretical Cosmology
  The learner can explain the core terms of Research Seminar in Theoretical Cosmology.
  The learner can distinguish related ideas inside Research Seminar in Theoretical Cosmology.
2. MethodsMethods in Research Seminar in Theoretical Cosmology
  The learner can apply a method from Research Seminar in Theoretical Cosmology to a documented case.
  The learner can select an appropriate method from Research Seminar in Theoretical Cosmology for a stated problem.
3. ApplicationApplication of Research Seminar in Theoretical Cosmology
  The learner can evaluate a practice of Research Seminar in Theoretical Cosmology against a stated criterion.
  The learner can transfer Research Seminar in Theoretical Cosmology to a new documented context.
06Coding the Cosmos: Python and C++ for Physics
1. FoundationsFoundations of Coding the Cosmos: Python and C++ for Physics
  The learner can explain the core terms of Coding the Cosmos: Python and C++ for Physics.
  The learner can distinguish related ideas inside Coding the Cosmos: Python and C++ for Physics.
2. MethodsMethods in Coding the Cosmos: Python and C++ for Physics
  The learner can apply a method from Coding the Cosmos: Python and C++ for Physics to a documented case.
  The learner can select an appropriate method from Coding the Cosmos: Python and C++ for Physics for a stated problem.
3. ApplicationApplication of Coding the Cosmos: Python and C++ for Physics
  The learner can evaluate a practice of Coding the Cosmos: Python and C++ for Physics against a stated criterion.
  The learner can transfer Coding the Cosmos: Python and C++ for Physics to a new documented context.
07Debugging and Optimization for Scientists
1. FoundationsFoundations of Debugging and Optimization for Scientists
  The learner can explain the core terms of Debugging and Optimization for Scientists.
  The learner can distinguish related ideas inside Debugging and Optimization for Scientists.
2. MethodsMethods in Debugging and Optimization for Scientists
  The learner can apply a method from Debugging and Optimization for Scientists to a documented case.
  The learner can select an appropriate method from Debugging and Optimization for Scientists for a stated problem.
3. ApplicationApplication of Debugging and Optimization for Scientists
  The learner can evaluate a practice of Debugging and Optimization for Scientists against a stated criterion.
  The learner can transfer Debugging and Optimization for Scientists to a new documented context.
08Building Your First Computational Model
1. FoundationsFoundations of Building Your First Computational Model
  The learner can explain the core terms of Building Your First Computational Model.
  The learner can distinguish related ideas inside Building Your First Computational Model.
2. MethodsMethods in Building Your First Computational Model
  The learner can apply a method from Building Your First Computational Model to a documented case.
  The learner can select an appropriate method from Building Your First Computational Model for a stated problem.
3. ApplicationApplication of Building Your First Computational Model
  The learner can evaluate a practice of Building Your First Computational Model against a stated criterion.
  The learner can transfer Building Your First Computational Model to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a theoretical physicist who believes that the cosmos can be understood through the language of computation. My work involves designing and running complex simulations to test the fundamental laws of the universe, bridging the gap between abstract theory and observable phenomena.

Applied mentorship

I am a computational physicist who specializes in bringing abstract theories to life through code and data. My mentorship is focused on empowering students to build, test, and visualize their own models, bridging the chasm between physics and computation.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

• Blog Post: 'Why the Universe is a Computer' - An accessible essay exploring the computational theory of the universe and its implications for modern physics. · • Blog Post: 'Simulating the Big Bang on Your Desktop' - A guide to the computational challenges and triumphs of modeling cosmic events. · • Conference Paper: 'A New Method for Simulating Quantum Entanglement in Multi-Particle Systems' - Presented at the International Conference on Quantum Computing, outlining a novel algorithm. · • Journal Article: 'Numerical Solutions to the Einstein Field Equations for Binary Black Hole Mergers' - Published in Physical Review Letters, detailing a breakthrough in gravitational wave modeling. · • Standard Article: 'The Search for Dark Matter: From Theory to Simulation' - A multi-part series for a popular science journal on the computational methods used to hunt for dark matter. · • Book: 'Computational Cosmology: The Universe in Code' - A comprehensive textbook on using high-performance computing to model the large-scale structure of the universe. · • Total Score: 29/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The First Steps in Computational Physics: A Guide for Beginners' - A short, practical guide for new students on setting up their first physics simulation environment. · • Guide: 'Using Python to Visualize Gravitational Waves' - A hands-on tutorial for students on how to use open-source libraries to plot and analyze real-world gravitational wave data. · • Essay: 'The Future of Physics is Open Source' - An essay on the importance of collaborative and open-source software in accelerating scientific discovery. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Spacetime Simulation

Adaptive capability

Mentor superpower

GAF-powered Code-to-Cosmos Translation

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Victor Ravenscroft

Theoretical Cosmology, Particle Physics, Quantum Field Theory, High-Performance Computing

Meet your professor Open the classroom

AI Super Mentor

Dr. Alessandra Fontana

Computational Physics, Quantum Computing, Scientific Programming, High-Performance Computing

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master	Doctorate This programme
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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PhD in Theoretical Cosmology and Particle Simulations

Academic focus

Practical focus

JPL (Jet Propulsion Lab)

Computational Physicist

Data Scientist

Aerospace Engineer

Research Scientist

Problem-Solving

Programming

Critical Thinking

Communication

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Victor Ravenscroft

Dr. Alessandra Fontana

Find the program that expands your universe.