whynotme23
Dec 8, 2022
Graduate / Masters in mathematical engineering [2]
PROMPT:
Discuss career strategy, academic goals and what you hope to achieve through your master studies in under 1000 words.
It was in the second year of my undergraduate degree that I was introduced to the computational paradigm during a course on the principles of flight. I was impressed by how we can model and visualize the airflow around an airfoil to calculate forces, without ever requiring a prototype or a WindTunnel. But what fascinated me most were the methods used to reduce a set of unsolvable governing differential equations, to a set of algebraic equations that a computer could solve and produce remarkably realistic results. This curiosity resulted in me taking courses on computational fluid dynamics, finite element analysis and implementing numerical solvers in future semesters. As part of my capstone project, I made extensive use of a commercial FEM solver to run simulations of a model that involved electrical and thermo-fluid interactions. I was amazed at how complex real-world phenomena could be broken down into a set of interacting physics, where individual interactions are governed by laws. This is at the core of why I grew interested in computational science. I want to write such software to explore various modelling and simulation approaches and solve problems involving fluids, heat transfer and other physics in engineering and scientific applications. At, EPFL I intend to learn more about algorithms, high-performance computing, simulation techniques and further hone my r&d skills through the master's program in computational science and engineering.
I'm looking forward to taking the courses on multiprocessor architecture, parallel programming and scientific computing. Making sure your code can scale is an important part of every computational solution and will without a doubt be useful in any project I might do in the future. A lot of legacy CFD/FEM code can benefit from parallelization and even commercial codes suffer from bottlenecks that prevent speedup to the maximum extent. I would love to be a part of the projects at labs like IAG, CMCS, EFLUM that try to introduce parallelization into existing algorithms, experiment with different numerical schemes and apply these techniques to solve domain problems. As someone who's interested in fluid simulation, I will be taking ME-474, ME-467 and MATH-468 from lists A and C. This along with the other courses in List C will provide a solid basis for any future research or internship. I will also be taking List D as AI/ML and statistics can be a useful asset in any r&d. Data driven and physics informed simulation of fluids and other dynamical entities have emerged as an alternative to traditional model based numerical simulation. Having studied these methods (especially PINNs) in the past, I would be interested in further research in this area of scientific ML. The ML4Science initiative as part of the ML Course (CS-433) seems like a great place to start.
During my undergrad, I completed courses in all of the basic sciences and also incorporated as many math and math-heavy courses as I could. Aside from being at the top of my class in my chosen electives, I often undertook side projects and self-studied subjects outside the prescribed curricula that I found interesting. I remember studying nonlinear dynamics by steven Strogatz from cover to cover, and this was one of the key reasons I decided to pursue interdisciplinary research and applied math. All in all, I believe I'm well prepared for graduate-level coursework on fluid dynamics, numerical analysis and for pursuing interdisciplinary research.
In my final year, I was on the lookout for projects and internships where I could work on interdisciplinary research involving modelling and simulation. I was selected as a research student and tasked with optimising the placement of components on a PCB to ensure minimal heating. I first built a Multiphysics model of the PCB to collect data. I then designed and trained a neural network using TensorFlow to predict temperature distribution on the board. This was then used as part of a continuous genetic algorithm, which would optimize the placement. I coded the full workflow using python and, in the process, learnt a lot about simulation strategies, modelling approaches, evolutionary optimization as well as softer skills like collaboration, conducting literature surveys and publishing a paper.
After taking a course on the mathematics behind machine learning I was keen to explore more practical aspects of it. I managed to bag an internship at a start-up as a machine learning engineer. I was tasked with modifying and training an object detection algorithm and integrating it with their platform. After graduating I took up a job as a software developer, and my job for the most part involves automating tasks with python and shell scripts. With the practical programming knowledge that I acquired from these professional experiences and my previous theoretical knowledge of data structures and algorithms, I'm confident of being able to implement good quality software solutions.
Upon completing my degree, I plan on working as a developer at companies like COMSOL, Altair etc. i.e., companies that build commercial software solutions to solve engineering problems. There are also a multitude of start-ups working in the modelling and simulation space as computational tools are starting to be more widely used in design and decision making. I would also look out for such opportunities as they often work on interesting and impactful ideas. While I want to go into industry as of now, I also want to keep the option of pursuing a PhD open. Completing semester projects and a thesis in areas I'm passionate about like scientific machine learning and numerical simulations will prepare me adequately for a Ph.D. The programme offered at EPFL is well-balanced and it contains the necessary skills required to either pursue a PHD or enter industry. I'm confident that I possess the prerequisite math, programming and modelling skills and I hope you find my previous work experience and academic qualifications well-suited. I'm well aware of the prestige of EPFL and the high standards it expects of its students and I'm ready to give it my all to reach my objectives and provide value to the institution in doing so.
PROMPT:
Discuss career strategy, academic goals and what you hope to achieve through your master studies in under 1000 words.
It was in the second year of my undergraduate degree that I was introduced to the computational paradigm during a course on the principles of flight. I was impressed by how we can model and visualize the airflow around an airfoil to calculate forces, without ever requiring a prototype or a WindTunnel. But what fascinated me most were the methods used to reduce a set of unsolvable governing differential equations, to a set of algebraic equations that a computer could solve and produce remarkably realistic results. This curiosity resulted in me taking courses on computational fluid dynamics, finite element analysis and implementing numerical solvers in future semesters. As part of my capstone project, I made extensive use of a commercial FEM solver to run simulations of a model that involved electrical and thermo-fluid interactions. I was amazed at how complex real-world phenomena could be broken down into a set of interacting physics, where individual interactions are governed by laws. This is at the core of why I grew interested in computational science. I want to write such software to explore various modelling and simulation approaches and solve problems involving fluids, heat transfer and other physics in engineering and scientific applications. At, EPFL I intend to learn more about algorithms, high-performance computing, simulation techniques and further hone my r&d skills through the master's program in computational science and engineering.
I'm looking forward to taking the courses on multiprocessor architecture, parallel programming and scientific computing. Making sure your code can scale is an important part of every computational solution and will without a doubt be useful in any project I might do in the future. A lot of legacy CFD/FEM code can benefit from parallelization and even commercial codes suffer from bottlenecks that prevent speedup to the maximum extent. I would love to be a part of the projects at labs like IAG, CMCS, EFLUM that try to introduce parallelization into existing algorithms, experiment with different numerical schemes and apply these techniques to solve domain problems. As someone who's interested in fluid simulation, I will be taking ME-474, ME-467 and MATH-468 from lists A and C. This along with the other courses in List C will provide a solid basis for any future research or internship. I will also be taking List D as AI/ML and statistics can be a useful asset in any r&d. Data driven and physics informed simulation of fluids and other dynamical entities have emerged as an alternative to traditional model based numerical simulation. Having studied these methods (especially PINNs) in the past, I would be interested in further research in this area of scientific ML. The ML4Science initiative as part of the ML Course (CS-433) seems like a great place to start.
During my undergrad, I completed courses in all of the basic sciences and also incorporated as many math and math-heavy courses as I could. Aside from being at the top of my class in my chosen electives, I often undertook side projects and self-studied subjects outside the prescribed curricula that I found interesting. I remember studying nonlinear dynamics by steven Strogatz from cover to cover, and this was one of the key reasons I decided to pursue interdisciplinary research and applied math. All in all, I believe I'm well prepared for graduate-level coursework on fluid dynamics, numerical analysis and for pursuing interdisciplinary research.
In my final year, I was on the lookout for projects and internships where I could work on interdisciplinary research involving modelling and simulation. I was selected as a research student and tasked with optimising the placement of components on a PCB to ensure minimal heating. I first built a Multiphysics model of the PCB to collect data. I then designed and trained a neural network using TensorFlow to predict temperature distribution on the board. This was then used as part of a continuous genetic algorithm, which would optimize the placement. I coded the full workflow using python and, in the process, learnt a lot about simulation strategies, modelling approaches, evolutionary optimization as well as softer skills like collaboration, conducting literature surveys and publishing a paper.
After taking a course on the mathematics behind machine learning I was keen to explore more practical aspects of it. I managed to bag an internship at a start-up as a machine learning engineer. I was tasked with modifying and training an object detection algorithm and integrating it with their platform. After graduating I took up a job as a software developer, and my job for the most part involves automating tasks with python and shell scripts. With the practical programming knowledge that I acquired from these professional experiences and my previous theoretical knowledge of data structures and algorithms, I'm confident of being able to implement good quality software solutions.
Upon completing my degree, I plan on working as a developer at companies like COMSOL, Altair etc. i.e., companies that build commercial software solutions to solve engineering problems. There are also a multitude of start-ups working in the modelling and simulation space as computational tools are starting to be more widely used in design and decision making. I would also look out for such opportunities as they often work on interesting and impactful ideas. While I want to go into industry as of now, I also want to keep the option of pursuing a PhD open. Completing semester projects and a thesis in areas I'm passionate about like scientific machine learning and numerical simulations will prepare me adequately for a Ph.D. The programme offered at EPFL is well-balanced and it contains the necessary skills required to either pursue a PHD or enter industry. I'm confident that I possess the prerequisite math, programming and modelling skills and I hope you find my previous work experience and academic qualifications well-suited. I'm well aware of the prestige of EPFL and the high standards it expects of its students and I'm ready to give it my all to reach my objectives and provide value to the institution in doing so.