When applying for academic jobs, it is common to apply to multiple positions and use essentially the same cover letter for all the applications. The danger is that you might forget to change some of the details from one cover letter to the next. I realized that I have done this recently. How bad is it? Specifically, I applied to a call in area X by saying I’m applying to area Y. (Thankfully, I got the name of the university right. And X and Y are not very different.)

Perhaps someone who has experience on hiring committees can tell me how much of a detriment it is to an application to have the wrong details in a cover letter. I assume this is a reasonably common mistake that is largely overlooked, but it gives the wrong first impression.

I’m heading a group of engineering students (from different fields) which need to build a robot (which performs tasks autonomously) as part of their courses.

There are 10 students in this group and they are divided in three or four subgroups which work on different engineering aspects. As the time frame is pretty short (2.5 months) and the students have a lot of other things to tend to, I wanted to optimize their work a bit by using some tools for project management and communication.

As I have recently been around a lot of IT people, I got to know about some of the methods and tools they use in the organization of small to medium-ish sized(startup) companies. So my question boils down to:

Is it advisable to manage short term – medium/high workload student projects using productivity and management tools(e.g. Slack?) and techniques, or could this actually be of little use to the project(especially if the supervisor has no experience using them)?

I’m working at a research institute in Germany, which offers the possibility to pursue a PhD degree provided that one can find a topic, which is not very unrelated to the ongoing projects, and a professor at a German university who can supervise the thesis. I should note that PhD supervision in Germany (unlike US), especially of “external” PhDs, where one does not carry out the work at a university chair, does not mean much more than occasional meetings with your professor (1-2 per year), and the official connection to a university so that you can present your thesis to a committee in the and actually obtain a degree in the end.

In my case, I’m coming from an electrical engineering / telecommunications background; I studied extensively in hardware-related areas and took lots of courses in wireless communications and signal processing. Although I took several classes in signal processing, some image processing courses, and some classes in mathematics (e.g. calculus, linear algebra, probability) during my bachelor’s and master’s studies, I lack the typical computer science background where one is exposed to courses like data structures, algorithms, and specialized software engineering courses. I also never took a formal computer vision course at the university.

I recently developed a passion for computer vision, and I’m convinced that it is the right path where I would like to develop myself, publish, and obtain a PhD degree. I’m planning on building an academic career around computer vision. However, when I look at and go through the papers from top-tier academic computer vision conferences such as CVPR, ECCV and ICCV, I realized that although I can follow most of the arguments and math in the papers, I certainly need some fundamental academic knowledge on computer vision if I want to produce such publications myself. Based on my technical abilities, I don’t think that creating the necessary (research) software would be the main problem but rather, coming up with the algorithms and making effective use of the building blocks of computer vision would be very challenging for me. Especially, some necessary techniques from machine learning and optimization are not very familiar to me. Moreover, although I have some up-to-date knowledge about the recent methods such as deep learning with convolutional neural networks, I do not really know the working details of the older, classic algorithms (e.g. SIFT, SURF, HOG) that could have many applications under different branches of vision.

Considering my background, how would you recommend me to proceed on my way to doing research on computer vision? More concretely, should I first go through a full computer vision course online (unfortunately, since I’m not at a university, I can’t go on and attend to a real lecture), or should I first try to fill the wholes in my more fundamental knowledge such as machine learning, optimization, and matrix computations/linear algebra? Another (more practical) possibility would be to go through an OpenCV book such as this one, and develop an all-around working knowledge more quickly. I’m more apprehensive about this approach since I’m afraid it would not give me the more solid theoretical knowledge that I need to be able to come up with creative solutions to computer vision problems. How do you think one should approach to the balance between practical and theoretical knowledge from a beginner’s point of view?

My second question is: How can I find an interesting problem in a certain subfield of computer vision, which I could take on as a PhD topic? I have already figured out that I’m more interested in the semantic understanding of images/videos rather than geometrical or 3D scene modelling. For example, I like problems such as multiple person tracking and pedestrian detection. However, I’m having a hard time coming up with a concrete research problem that I will devote my attention to and bring together in the end as a coherent PhD thesis. This confusion is mostly caused by the lack of a supervision from an experienced researcher, since I don’t yet have an official supervisor/professor who would give me a research topic. I know that is unusual, and makes one question whether obtaining a PhD is feasible at all in my current research environment, but that is just how the things are. Unfortunately, in my situation, it is hard to approach a professor without any concrete research proposal and even some initial results in hand.

Thank you for your time and kind interest, and I apologize for any possibly vague parts in my question. I would be happy to clarify and elaborate.

Doing literature review for my Master thesis, I have got in touch with many different types of scientific papers, meaning publications, working papers, conference papers, seminar papers and so on.

I have seen many discussion about sub-categories of these main objects but never one being at this higher level of grouping.

Thus, I was wondering which are the main difference among them in order to do some kind of hierarchy of content reliability when I read them. And clearly if there exist other types, please add so we can build a complete collection

Here is what I feel whenever I find something interesting and feel like pursuing it :

  1. Oh so I like X (Computer Graphics), let me read up papers/books about it.
  2. Ok let me begin with reading up Y (OpenGL)
  3. But Y needs W (Linear Algebra)
  4. Well reading up Z (Probability) first makes more sense.
  5. Umm, you shouldn’t jump to Z without learning U (Permutation/Combination).
  6. And how come I forget about reading V (Number theory)
  7. And what not..

I always end up searching and reading up “Best books to begin A/B/C/D..” instead of actually making myself begin somewhere. This consumes all my energy and I never really start.

Q. Have others faced this ? Q. How do you handle this and actually begin somewhere ?

Any help would be really appreciated.

Background : I am a working professional, with Masters in Computer Science (fascinated with Computer Graphics etc). Its been two years since my masters but I still kind of miss academia, my thesis work and other interesting stuff I did there. My current work is also pretty interesting and partially overlaps with my interest areas. However, other than work, I would really want to continue doing things related to my masters side by side (and MAY BE take up a Phd somewhere down the line). But the never ending feeling of not knowing anything takes over.

Its not that I am being forced to study any of this. Its purely for my personal interests that I want to pursue it.

I wanted to peruse a Masters, in ‘Data mining’ or related.

I have a budget of (U$S 12,000 per year) including housing, fees, food expenses, medical insurance, etc. Is there any European University that I could apply with this personal budget?

(I hold a EU passport).

I am not looking for a list… There are not many data mining masters, and I also have a budget constraint… Also ‘online’ is not an option for me, since I prefer attending to real classes, and I am also willing to move to get a more international experience.

Information on the internet is not clear, hence my question.

I have read that masters in Denmark and Sweden are free for Europeans. I am from Spain, so I could potentially apply.

My questions:

  • is there any contract where the student has to live/work in the country for a certain period before/after studying/applying for the masters?

  • If I don’t finish the master, do I have to pay for it?

  • It sounds too good to be true… if that’s the case, how come all Europeans don’t go to study to Denmark/Sweden? Why would any British, Italian, etc study outside Denmark/Sweden?

So far, my master thesis had an initial plan, and I would keep in contact with the PI as I need. However, now that the academic course comes to an end, all the students in the lab (3) we have to finish our projects. He is also applying to grants that have tight deadlines and supervising 1 PhD thesis that will be presented in a month, together with addressing the reviewers of two papers and supervising a new one (with part of my work :). This is a lot of work (I am sure other people are more stressed though) and sometimes I feel I am not given all the advice I could due to poor project management of the lab and projects (yes, I tried more frequent meetings but they keep being postponed).

I would like to start a PhD soon, and later to have my own research team; what project management skills should I learn for a successful PhD/career?
Some skills I thought as important are:

  • Time management

    I found useful answers here at the tag time-management

  • Multiple (parallel) projects management

    My current project started as to analyze some data and ended up analyzing data and developing, testing and prove a new method. (That’s another reason why I consider my project was poorly thought out.)

  • People management
  • ?

And more importantly, how can I learn them through the master thesis and PhD?