I am a recent fresh graduate who came from math and physics. Recently (< 4 weeks), I’ve taken on a junior research role in a research institute within a University.
This particularly university – and those in this country – have extremely strong collaborations with industry partners so work in Universities in this country is divided into pure/ fundamental research and industry-driven research. The University houses corporate research labs.
The project I am working on right now is in the area of air traffic. I report to a fellow (academic title) who comes from industry who is only focused on research in operations/ convincing stakeholders and sponsors.
Research because everything he proposes and investigates are qualitative and conceptual without any justifications with mathematics in an area which presumably requires mathematical models to validate ideas.
Granted that I have been in this role for <4 weeks, I am miserable and I believe I will continue to feel so.
I believed I was overpromised.
I was promised that I would be working alongside professors to develop new mathematical models for air traffic.
It turns out I am the only person from math and physics. The rest are from engineering or industrial design field.
Much of my time would be involved with producing white papers and policy recommendations for management of future air traffic
3) I am pushed to pursue a PhD (comes with scholarship) which, with internal discussion, would allow me to move into a PhD program even with an average BSc grades since the director of the research institute is the PI for the PhD program.
However, my thesis would have to be in the area of air traffic.
My interest is in Abstract Algebra, Topology and Quantum mechanics and I’ll like to pursue this in a year or two time when I return back to University.
4) The fellow I report too is really a project manager in disguise. Every discussions with him mainly revolves around stakeholder expectations and targeted objectives.
A good case scenario is this: He engages a visiting scholar who has done a PhD thesis in air traffic modelling, look to see how the idea in the thesis could be extended to specific circumstances (without much modification to the mathematical model in the thesis) for national interest before suggesting a feasibility write up on why this works.
From here, it is already obvious that nothing of substance could be published in respectable journals.
I am afraid that my current research role will not provide me with a stronger exposure to math or physics in the fundamental sense.
What is the best course of action?
I am a PhD student working in theory (computer science). I am confused about one thing, and I am only the researcher in my group. I work on 2-3 small problems which I am trying to combine and publish. Although my results on these 2-3 problems are not big (the idea is also not big), I have tried to solve 2-3 small problems and have gotten a result that makes a paper, according my colleagues in the university. I have seen researchers (very senior) whose work seems to be very non-trivial to me (as compared to me). I am confused about this: should I work on one particular problem for a year and get a result (there is a high risk with this thing) or should I work on 2-3 small problems and combine them to make a result?
Question: For a PhD student, what is good about working on a few small problems vs solving a big problem? I have heard and have seen the profile of some professors in which they say/write that PhD students (mostly) do survey kind of things. I don’t know this is true or not.
I was admitted to several Phd position and I am considering most interesting two labs. Their fields are similar and both interesting to me. The problem is, one lab has more general topic, but the other has more specific topic.
The ‘A’ lab is focusing on brain-inspired AI. It aims to develop deep learning model based on neuroscience. There is not much rooms for mathematics seeing the previous papers. The ‘B’ lab is more concentrated on machine learning itself and uses extensive mathematics.
After I get Phd in CS, I will probably go to industry or perhaps to academia and I should be able to apply my knowledge to new domains which is required by company. I think mathematics is important for this flexibility.
I am worrying that if I study brain-driven AI during Phd, I might lack flexibility since the topic is specific and it is not using math a lot. Yes, I know that as a Phd, I should study mathematics by myself required for machine learning but it is also true that student whose topic is more math oriented ML will relatively have better knowledge at the end of Phd.
Some give me advice that I should go to a lab having more broad field of study. And it is better to go to more specific fields as a post-doctor. Considering the fact that I should always study new things by myself after Phd, I think it would be better to be trained with extensive mathematics which is core baseline of ML.
What do you think?
I read a very recent paper that describes something I am only starting to study. Basically, it looks like authors of the paper have covered almost everything that can be explored in the topic.
How can I know if there is room left to research the topic? Should I approach the authors with a similar question?
Is there an aggregated pool of potential thesis topics that are available for exploration across universities for different concentrations? Perhaps there would be some that have been pre-vetted by professors.
I’m curious if there’s an easier and/or quicker way to discover and select topics when there is not a compelling subject/topic available.
I need a scientist/researcher in the energy engineering field, to help me out.
my advisor won’t help me directly and refers me to his ph.d students and they’re kinda codger, and don’t have any vision of what’s going on!
I need some advice from an energy researcher to help me
first of all I can’t find a distinction between energy conversion (mechanical eng.) and energy systems eng.
my b.sc is mechanical engineering but I’m excited into process side of energy engineering but I’m not sure about what to do!
also I’m looking forward to pursue my study and get a fellowship from eu-universities for sake of this, I need to publish papers of my thesis and work really hard, and as a hard-worker I should know about the thing I’m going to choose as my speciality.
thank you for reading this ladies and gentlemen 🙂
Based on your personal experience, how may someone enter a completely new research area as a 3rd-year Ph.D. candidate? Is it better to start with reading journal papers? Or maybe taking some courses? Or watching YouTube videos? Or what?! I am basically looking for the most efficient/fastest way of doing this!
And if you want to know my story, I am a Ph.D. candidate in structural engineering with a focus on deterministic earthquake modeling, but for some reasons now my advisor wants me to start working on probabilistic modeling of hurricanes and floods using machine learning methods, and I am a little paranoid because I have no idea about any of these! So what are your suggestions?!
I have two topics that I really want to get involved in.
1) Confirming current research in chromosome structure while using machine learning to find relationships in genetic motifs. For instance, is function associated with how the genome condenses?
2) Developing novel Computer Vision algorithms to learn from small sample sizes and video using specialized equipment.
Maybe there is a common problem between 1) and 2) that I can study in depth, but these seem like very different projects.
Would it be feasible to apply for a Master’s in Quantitative Genetics (+ Research Thesis) with electives in Machine Learning and Statistics, then apply for a PhD in Machine Learning? I have a double major in Math and Computer Science with a few electives in genetics.
I want to start a PhD in Computer Science, having successfully finished my BSc and MSc. My main areas of focus are data mining, time series, machine learning and big data.
What are the current hot topics that I can base my research on, mainly focusing on the areas I mentioned above?
Thanks for your help!
One semester ago I started my Phd in applied mathematics which is 3 years. My bachelor and master was on applied math and analysis of partial differential equations respectively.
I am starting to think that I will not make it at a math centered topic because math seem very difficult to me and I don’t enjoy anymore trying to understand them. As a consequence I am trying to find some topics that will be more interesting to me and less mathy. So I am thinking of doing a phd in computational fluid dynamics ( staying in the math department but choosing an advisor from the aeronautical and engineering department) because I have read some things about it and it seems an interesting topic with a possibility to work in the industry (which I want to do) after the end of the Phd.
The problem is that my knowledge in coding is scarce and I am not sure that I can be good enough at it as to produce a thesis. The professor told me that it can be done but it will require a lot of work. Do you think is a good idea to start my phd on this topic given the fact that i don t now how to code?