Thanks for the reply statguy. By physics related, I meant the second option you stated. I am already a data mining analyst, so building a data scientist career is not a big challenge for me. But I developed interest in science so i want to make a career in physics or geosciences. I cannot afford to start over from the beginning(bachelors), hence I am planning to do a masters in data science with physics or geoscience concentration. I am fine with combining machine learning and any physics stream as long as I do actual physics(or geosciences).StatGuy2000 said:If you are talking about #2, these may be more limited. Most work involving astrophysics or particle physics are based at a relatively small number of research institutions, either affiliated with universities or with national laboratories (think Fermi labs, CERN in Switzerland, etc.) Only a relatively small number of physics PhD graduates would in general work in these institutions. Statisticians and computer scientists specializing in machine learning have collaborated with physicists in these areas, but my speculation would be that these would be academics in these fields, so I have my doubts about whether a masters in data science would be enough.
Thanks for the response. I am expecting to learn physics along with my data science degree(2 years). Consider that if I pursue my machine learning degree along with physics, is that knowledge enough to pursue a career with physics? I am okay with extending my program's duration if I have to learn enough physics.mfb said:Many areas of physics have applications for machine learning, but it is unclear how many universities/research labs/companies will hire a machine learning expert without any background in physics for these tasks. Typically you have to understand the input and output in terms of physics. That is not too hard to learn, but it is additional training time. In addition, no physics background can make you less flexible in physics-focused jobs.
Thanks for the response, I haven't known about machine learning applications on medical physics until you said. I'll further research on the topic. Say if I study medical physics along with my degree for two years, would that be enough for me to get a job in a company or research lab? Is that enough knowledge to further pursue for my PhD?Choppy said:Machine learning is a hot topic in medical physics right now. Consider imaging problems such as tissue classification and segmentation or tumour identification, computer-assisted diagnoses, problems in optimizing treatment workflow, radiotherapy treatment plan development, or treatment outcome prediction... these are all areas that stand to gain from machine learning.
And maybe this is pointing out the obvious, but, one of the things about most kinds of "research-support" related jobs is that they're not likely to be permanent. Someone may have a project in a field of physics that requires expertise in machine learning, and when they do and get some kind of grant funding, they'll hire on the expert, but eventually when the project is completed (or they run out of funding) they and you will have to move on. So aiming to support a particular field is probably not the best plan.
quantknight said:Say if I study medical physics along with my degree for two years, would that be enough for me to get a job in a company or research lab? Is that enough knowledge to further pursue for my PhD?
I don't know about medical physics, I only know how the jobs look like in particle physics and related fields.quantknight said:Thanks for the response. I am expecting to learn physics along with my data science degree(2 years). Consider that if I pursue my machine learning degree along with physics, is that knowledge enough to pursue a career with physics? I am okay with extending my program's duration if I have to learn enough physics.
Choppy said:From what I understand so far, you've done a degree in something that's not physics. You're qualified to get into a master's program in data science or machine learning and want to enroll, but are interested in doing work in physics or geoscience areas, and want to know if you can pick up enough physics on the side to break into the field, and potentially enroll in a PhD.
Getting into a PhD in physics (or medical physics) generally requires a bachelor's degree in physics, or it's equivalent. This isn't the level of knowledge that can be typically picked up on the side, while studying something else. And in most MSc programs, you'll be pretty busy studying the MSc material. Most MSc programs don't allow you to pick up a minor anyway. Some medical physics programs are little more flexible in terms of the background of people they'll accept. So you could potentially look into doing a master's in medical physics that involves a machine learning project, but again, without a bachelor's degree in physics or something very close to it, this will be an uphill battle.
Getting hired to do machine learning work in one of these fields though, can depend a lot more on your experience in the area. If you were to get an MSc in machine learning and apply for a research support position on a project that's looking at say, using machine learning algorithm to reduce noise in CT image reconstruction, whether or not you get hired will probably depend a lot more on your ability to work with the specific algorithms and data sets that the research group needs you to work with than your general background medical physics.
Thanks for this resource. This is good. I will look into it.mfb said:I don't know about medical physics, I only know how the jobs look like in particle physics and related fields.
Consider this list: Physics jobs and PhD positions somewhere close to particle physics that mention machine learning. Have a look at their requirements - you'll see a PhD (or MSc/BSc for PhD positions) in physics nearly everywhere. It is not impossible to apply with a different degree than listed there, but then you have to be an excellent candidate to have a chance. Typically these jobs have machine learning as a big part of the work, but not as only topic. In experimental physics it is expected that people contribute to the detector operation in some way, for example.
quantknight said:? If I take data science master's with minor in particle physics or astrophysics, what are the possibilities that I would be employed as a physics related data scientist?
Vanadium 50 said:Zero. Sorry, but that's how it is.
Mfb was too kind. Every single position (other than a graduate studentship) requires a PhD. Virtually all places that hire postdocs cannot hire someone who is not a postdoc into that position. This is because their grant application specifically mentions "postdoc", and you have to use the money the way you say you will. As far as industry, the amount of particle physics and astrophysics being done my industry is microscopic. As discussed above, expanding this into areas where there are commercial applications might change things, but otherwise, you are chasing jobs that do not exist.
quantknight said:Thanks for the reply. What about other topics in physics? I guess there would be industry jobs in machine learning with geophysics, medical physics,quantum information or optical information,etc? I feel it would be much better to do data analysis of physics or geoscience, rather than banking products or customer behavior. I would be happy if there is a chance to proceed into research in physics, but if it is not possible, I am fine with physics industry jobs which requires masters level education. (I posted this question based on the fact that many universities offer curriculum data science with business, banking or AI specialization, so I wanted to know about having a specialization in physics or geoscience).
Machine learning in particle physics is a branch of artificial intelligence that involves the development of algorithms and statistical models to analyze and interpret data from particle physics experiments. It is used to identify patterns and relationships in large datasets and make predictions about the behavior of subatomic particles.
Machine learning is used in particle physics research to analyze vast amounts of data collected from particle accelerators and detectors. It can help identify new particles, measure their properties, and improve the accuracy of theoretical models. It also aids in data filtering, event reconstruction, and background rejection.
The benefits of using machine learning in particle physics include increased efficiency in data analysis, improved accuracy in measurement and prediction, and the potential for new discoveries. It also allows for the exploration of complex datasets that would be difficult or impossible to analyze with traditional methods.
Some examples of successful applications of machine learning in particle physics include the discovery of the Higgs boson at the Large Hadron Collider, the identification of top quark decays at the Tevatron, and the development of automated event selection algorithms for experiments such as Belle II and CMS.
Some challenges faced when using machine learning in particle physics include the need for large and diverse datasets, the difficulty in interpreting complex models, and the potential for bias in the data. Additionally, the integration of machine learning into traditional particle physics research workflows can be a challenge, as it requires specialized skills and resources.